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Functional synaptic circuits in primary visual cortex

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Functional synaptic circuits in primary visual cortex

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Content i

FUNCTIONAL SYNAPTIC CIRCUITS
IN PRIMARY VISUAL CORTEX

YATANG LI

A DISSERTATION
PRESENTED TO THE FACULTY
OF THE UNIVERSITY OF SOUTHERN CALIFORNIA
IN CANDIDACY FOR THE DEGREE
DOCTOR OF PHILOSOPHY
(PHYSIOLOGY AND BIOPHYSICS)

MENTORS: LI I ZHANG AND HUIZHONG WHIT TAO

MAY 2014

© Copyright by Yatang Li, 2014.
All rights reserved.
ii

Dedication
To my parents, my wife and my son, for their love and support.
iii

Acknowledgements
First and foremost, my deepest gratitude goes to my mentors, Dr. Li I Zhang and Dr. Huizhong
Whit Tao, for their excellent mentoring, constructive comments and warm encouragement
throughout my graduate study. Their vision for what is the key question in system neuroscience
drives my research to the right direction. Their insight for what is the key for the question helps
me finish my projects in an efficient way. As mentors, Li and Whit never lost the focus of big
picture, meanwhile they provide me constant help with any detail. It was under their
mentorship, I could develop logic thinking, scientific independence, and research skill.
Special thanks are to my committee members, Dr. Robert Farley, Dr. Jeannie Chen and Dr.
Alapakkam Sampath, for their regular guidance throughout my qualification exam and
dissertation defense. I really appreciate their generous support for my academic career.
I have greatly benefited from being a member of a lab where everyone helps and supports each
other like a family. I really had a fun time doing science here. I would like to thank Dr. Baohua
Liu for teaching me almost everything about animal surgery and in vivo electrophysiology, Dr.
Wenpei Ma, Mr. Chenjie Pan and Mr. Xiaolin Chou for helping me in collecting and analyzing
data, Ms. Leena Ali Ibrahim and Dr. Shengzhi Wang for helping me in molecular and imaging
technique.
I am particular grateful to have shared the most beautiful time with my wife, Lingyun Li, in
USC. Without her love and support, I would not have overcome the barriers in research and
life. My love also goes to my son for bringing so much happiness into this young family. I
would like to thank my parents and my brother for their unconditional love and support.
iv

Table of Contents
Dedication ................................................................................................................................. ii
Acknowledgements .................................................................................................................. iii
List of Figures ......................................................................................................................... xii
Abstract ................................................................................................................................... xv
Chapter 1: Introduction ......................................................................................................... 1
1.1. Motivation .................................................................................................................. 1
Methodology .............................................................................................................. 2
Visual system ............................................................................................................. 3
1.2.1 Receptive field .................................................................................................... 3
1.2.2 Orientation and direction selectivity ................................................................... 4
Visual pathway ........................................................................................................... 4
Organization ............................................................................................................... 6
Chapter 2: Relative Roles of Thalamic Excitation and Cortical Excitation in Processing
Visual Information .............................................................................................. 8
Introduction ................................................................................................................ 8
v

Methods ...................................................................................................................... 9
2.2.1 Viral injection ..................................................................................................... 9
2.2.2 Animal preparation ........................................................................................... 10
2.2.3 In vivo electrophysiology .................................................................................. 11
2.2.4 In vivo two-photon imaging guided recording .................................................. 13
2.2.5 Visual stimulation ............................................................................................. 13
2.2.6 Photostimulation ............................................................................................... 14
2.2.7 Data analysis ..................................................................................................... 15
Results ...................................................................................................................... 17
2.3.1 Optogenetic silencing of visual cortical circuits ............................................... 17
2.3.2 Scaling of orientation-tuned thalamocortical input........................................... 20
2.3.3 Intracortical excitation preserves direction tuning ............................................ 25
2.3.4 Intracortical excitation expands visual receptive field ...................................... 26
2.3.5 Tuning of dLGN neurons is unaffected ............................................................ 29
Discussion ................................................................................................................ 31
vi

Chapter 3: Synaptic Circuits Underlying Orientation Selectivity at Different Contrasts in
Layer 4 .............................................................................................................. 35
Introduction .............................................................................................................. 35
Methods .................................................................................................................... 37
3.2.1 Animal preparation ........................................................................................... 37
3.2.2 In vivo electrophysiology .................................................................................. 37
3.2.3 Visual stimulation ............................................................................................. 38
3.2.4 Data analysis ..................................................................................................... 38
3.2.5 Modelling .......................................................................................................... 39
Results ...................................................................................................................... 41
3.3.1 Contrast-dependent sharpening of orientation selectivity of excitatory neurons ..
........................................................................................................................... 41
3.3.2 Contrast-dependent excitatory and inhibitory synaptic responses .................... 45
3.3.3 An inhibitory synaptic mechanism underlying contrast-dependent sharpening of
orientation selectivity ........................................................................................ 47
3.3.4 Contrast-dependent broadening of orientation selectivity of inhibitory neurons .
........................................................................................................................... 51
vii

3.3.5 Trial-to-trial variability of synaptic responses .................................................. 55
Discussion ................................................................................................................ 56
3.4.1 Contrast invariance vs. contrast-dependent sharpening of orientation selectivity
........................................................................................................................... 56
3.4.2 Inhibitory contribution to contrast-dependent sharpening of orientation
selectivity .......................................................................................................... 57
3.4.3 Trial-to-trial variability of synaptic responses .................................................. 58
3.4.4 Contrast-dependent broadening of orientation tuning of inhibitory neurons .... 59
Chapter 4: Synaptic Circuits of Direction Selectivity in Simple Cell in Layer 4 ............... 61
Introduction .............................................................................................................. 61
Methods .................................................................................................................... 64
4.2.1 Animal preparation ........................................................................................... 64
4.2.2 In vivo electrophysiology .................................................................................. 64
4.2.3 Visual stimulation ............................................................................................. 64
4.2.4 Data analysis ..................................................................................................... 64
4.2.5 Neuron model.................................................................................................... 66
4.2.6 Simulation ......................................................................................................... 66
viii

Results ...................................................................................................................... 67
4.3.1 Direction selectivity of layer 4 neurons in mouse V1 ....................................... 67
4.3.2 Direction tuning of subthreshold response ....................................................... 69
4.3.3 Direction tuning of excitatory and inhibitory synaptic inputs .......................... 71
4.3.4 Inhibition sharpens direction selectivity ........................................................... 73
4.3.5 Temporal offset between excitation and inhibition .......................................... 75
4.3.6 Spatially asymmetric excitatory and symmetric inhibitory receptive fields ..... 77
4.3.7 Spatiotemporal offsets between excitation and inhibition contribute to direction
selectivity .......................................................................................................... 79
Discussion ................................................................................................................ 82
Chapter 5: Synaptic circuits for orientation selectivity during development in layer 4 ..... 88
Introduction .............................................................................................................. 88
Methods .................................................................................................................... 90
5.2.1 Animal preparation ........................................................................................... 90
5.2.2 In vivo electrophysiology .................................................................................. 90
5.2.3 Visual stimulation ............................................................................................. 90
ix

5.2.4 Data analysis ..................................................................................................... 90
5.2.5 Modelling .......................................................................................................... 91
5.2.6 Dynamic clamp ................................................................................................. 92
Results ...................................................................................................................... 93
5.3.1 Development of orientation selectivity in layer 4 excitatory neurons of mouse
visual cortex ...................................................................................................... 93
5.3.2 Synaptic inputs underlying orientation selectivity during development........... 95
5.3.3 An inhibitory mechanism for the developmental sharpening of orientation
selectivity .......................................................................................................... 99
5.3.4 Excitation and inhibition after dark rearing .................................................... 104
5.3.5 Development of inhibitory neuron tuning....................................................... 106
Discussion .............................................................................................................. 108
5.4.1 An inhibitory mechanism underlying orientation selectivity sharpening ....... 108
5.4.2 Circuit models for the development of orientation selectivity........................ 110
Chapter 6: Synaptic Circuits Underlying the Different Orientation Selectivity between
Simple and Complex Cells.............................................................................. 113
Introduction ............................................................................................................ 113
x

Methods .................................................................................................................. 115
6.2.1 Animal preparation ......................................................................................... 115
6.2.2 In vivo electrophysiology ............................................................................... 116
6.2.3 Visual stimulation ........................................................................................... 116
6.2.4 Data analysis ................................................................................................... 116
Results .................................................................................................................... 117
6.3.1 Simple cells showed stronger orientation selectivity than complex cell for spike
responses in mouse V1.................................................................................... 117
6.3.2 Simple cells showed stronger orientation selectivity than complex cells for
membrane potential responses ........................................................................ 119
6.3.3 Orientation tuning of excitatory and inhibitory synaptic inputs to simple cells and
complex cells .................................................................................................. 121
6.3.4 Inhibition mediated different orientation selectivity in simple cells and complex
cells ................................................................................................................. 124
6.3.5 Membrane filtering and inhibitory sharpening of blurred selectivity ............. 126
Discussion .............................................................................................................. 128
6.4.1 Classification of simple and complex cells ..................................................... 129
xi

6.4.2 Potential mechanisms underlying different orientation tuning of inhibition in
simple cell and complex cell ........................................................................... 129
6.4.3 Input-output function and different inhibitory mechanisms ........................... 130
6.4.4 Implications on cortical circuits ...................................................................... 131
References ............................................................................................................................. 132

xii

List of Figures
Figure 1.1 Illustration of in vivo electrophysiology .................................................................. 2
Figure 1.2 Visual pathway ........................................................................................................ 4
Figure 2.1 Optogenetic silencing of visual cortical circuits ................................................... 19
Figure 2.2 Linear amplification of orientation-tuned thalamocortical input .......................... 21
Figure 2.3 Summary of orientation tuning based on measurements of integrated charge of
excitatory currents ................................................................................................................... 23
Figure 2.4 Intracortical excitation preserves direction tuning ................................................ 24
Figure 2.5 Summary of direction tuning based on measurements of integrated charge of
excitatory currents ................................................................................................................... 26
Figure 2.6 Intracortical excitation expands visual receptive field .......................................... 27
Figure 2.7 Orientation tuning of thalamic neurons ................................................................. 30
Figure 3.1 Contrast-dependent changes of orientation selectivity of layer 4 excitatory neurons
in mouse visual cortex ............................................................................................................ 43
Figure 3.2 Excitatory and inhibitory synaptic responses revealed by voltage-clamp recordings
................................................................................................................................................. 46
Figure 3.3 Contrast-dependent changes of synaptic inputs to excitatory neuronsion ............. 48
xiii

Figure 3.4 A broadening of inhibitory tuning primarily contributes to the contrast-dependent
sharpening of orientation selectivity ....................................................................................... 50
Figure 3.5 Contrast-dependent broadening of spike response tuning of PV inhibitory neurons
................................................................................................................................................. 52
Figure 3.6 Trial-to-trial variability of synaptic responses ...................................................... 54
Figure 3.7 A simple model for contrast-dependent sharpening of excitatory neurons and
contrast-dependent broadening of PV inhibitory neurons ...................................................... 60
Figure 4.1 Direction selectivity of layer 4 neurons in mouse V1 ........................................... 68
Figure 4.2 Direction tuning of subthreshold membrane potential (Vm) response ................. 70
Figure 4.3 Direction tuning of excitatory and inhibitory synaptic inputs ............................... 72
Figure 4.4 Inhibition sharpens direction selectivity of membrane potential response. .......... 74
Figure 4.5 Temporal and spatial offsets between excitation and inhibition ........................... 76
Figure 4.6 Spatiotemporal offsets between excitation and inhibition contribute to direction
selectivity ................................................................................................................................ 80
Figure 5.1 Developmental sharpening of orientation selectivity in mouse visual cortex ....... 94
Figure 5.2 Synaptic inputs underlying orientation selectivity of developing layer 4 excitatory
neurons .................................................................................................................................... 97
xiv

Figure 5.3 The broadening of inhibitory tuning is a determinant synaptic mechanism
underlying the developmental sharpening of orientation selectivity .................................... 100
Figure 5.4 Effects of dark rearing and tuning of inhibitory neurons .................................... 105
Figure 6.1 Simple cells showed stronger orientation selectivity than complex cell for spike
responses in layer 2/3 of mouse V1 ...................................................................................... 118
Figure 6.2 Simple cells showed stronger orientation selectivity than complex cell for
membrane potential responses. ............................................................................................. 120
Figure 6.3 Orientation tuning of excitatory and inhibitory synaptic inputs to example simple
cells and complex cells ......................................................................................................... 123
Figure 6.4 Orientation tuning of excitatory and inhibitory synaptic inputs to simple cells and
complex cells ........................................................................................................................ 125
Figure 6.5 Membrane filtering and inhibitory sharpening of blurred selectivity ................. 127

xv

Abstract
One key question in system neuroscience is how the brain function is achieved by the dynamic
functional connection among different neurons. A neuron receives inputs from the synapses of
its connected neurons, and fires action potentials as its output. In the visual cortex, the
functional properties of a single neuron is defined by its action potentials, which are determined
by the integration of different synaptic inputs. However, what’s the roles of different synaptic
inputs for visual processing is poorly understood. Using mouse primary visual cortex (V1) as
a model, I dissected the functional synaptic circuits by applying in vivo whole-cell patch clamp
technique and optogenetic tools.
In my first project, we probed the relative contributions of thalamic excitation and cortical
excitation for orientation selectivity, direction selectivity and spatial receptive field in layer 4.
We silenced intracortical excitatory circuits with optogenetic activation of parvalbumin-
positive (PV+) inhibitory neurons, and compared visually evoked thalamic excitation with total
excitation in the same layer 4 excitatory neurons. We found that thalamic excitation is direction
and orientation selective, and shows slightly elongated spatial receptive field. Cortical
excitation preserves the orientation and direction tuning of thalamic excitation, with a linear
amplification of thalamocortical inputs of about threefold, and expands thalamic spatial
receptive field in an approximately proportional manner. Thus, intracortical excitatory circuits
faithfully reinforce the representation of thalamocortical information and influence the size of
the spatial receptive field by recruiting additional cortical inputs.
xvi

In my second project, we investigated the roles of synaptic inhibition for orientation selectivity
with different stimulus contrast in layer 4. We found orientation selectivity of spike response
is sharpened as contrast increased. The sharpening is caused by the scaling up of excitation
and broadening of inhibition. Modeling revealed that the broadening of inhibition is critical for
sharpening orientation selectivity from low to high contrast. Finally, broadening of inhibition
can be attributed to a contrast-dependent broadening of spike-response tuning of PV+ neurons.
Together the results indicate that modulation of synaptic inhibition sharpens orientation
selectivity during changes of stimulus strength.
In my third project, we explored the roles of synaptic inhibition for the development of
orientation selectivity in layer 4. We found orientation selectivity of spike response is
progressively sharpened during development. Synaptic excitation and inhibition strengthened
in a parallel way. Orientation tuning of excitatory inputs keeps relatively unchanged, whereas
the tuning of inhibitory inputs is broadened, and becomes significantly broader than that of
excitatory inputs. Neuron modeling and dynamic-clamp recording demonstrated that this
developmental broadening of the inhibitory tuning is sufficient for sharpening orientation
selectivity. Depriving visual experience by dark rearing impedes the normal developmental
strengthening of excitation, but a similar broadening of inhibitory tuning, likely caused by a
nonselective strengthening of inhibitory connections, results in the apparently normal
orientation selectivity sharpening in excitatory neurons. Our results thus provide the first
demonstration that an inhibitory synaptic mechanism can primarily mediate the functional
refinement of cortical neurons.
xvii

In my fourth project, we studied the synaptic circuitry underlying direction selectivity in layer
4. We found the simple cells receives direction-tuned excitatory input but barely tuned
inhibitory input. Excitation and inhibition exhibits differential temporal offsets under
movements of opposite directions: excitation peaks earlier than inhibition at the preferred
direction, and vice versa at the null direction. This can be attributed to a small spatial mismatch
between overlapping excitatory and inhibitory spatial receptive field: the excitatory spatial
receptive field was skewed and the skewness was strongly correlated with the direction
selectivity, whereas the inhibitory receptive field was relatively spatially symmetric. Neural
modeling revealed that the relatively stronger inhibition under null directional movements, as
well as the specific spatiotemporal offsets between excitation and inhibition, allows inhibition
to significantly enhance direction selectivity of output responses by suppressing the null
response more effectively than the preferred response. Our data demonstrate that while tuned
excitatory input provides a basis for direction selectivity, largely untuned and spatiotemporally
offset inhibition contributes significantly to sharpening the selectivity in mouse visual cortex.
Furthermore, amplitude-dependent suppression can sufficiently result in the direction
selectivity of excitation.
In my fifth project, we turned to layer 2/3 and examined the synaptic mechanisms underlying
orientation selectivity in simple and complex cells. We found simple cells are more orientation
selective than complex cells for both spike responses and membrane potential responses.
Inhibition is more broadly tuned than excitation in simple cell, and vice versa in complex cell.
Compared with complex cell, the excitatory tuning is similar and inhibitory tuning is broader
xviii

in simple cell. Thus our data showed that it is the synaptic inhibition rather than excitation that
shapes the orientation tuning in different types of neurons.
Taken together, in layer 4, cortical excitation faithfully reinforces the representation of
feedforward information, and synaptic inhibition plays a vital role in maintaining the sharpness
of feature selectivity. In layer 2/3, synaptic inhibition generates diverse selectivity in different
types of neuron.

1

Chapter 1: Introduction
1.1. Motivation
In the cortex, neuron is the elementary functional unit. The functional property of a neuron is
defined by its firing pattern of action potentials. Neurons connect with each other via synapse
to form neural circuits, which is responsible for sensing the stimuli and controlling the
behavior. The spike pattern of a neuron is determined by the synaptic inputs from its connected
neurons. A key question is how its different synaptic inputs integrate to affect the spike output
of a neuron.
For example, in the thalamic-recipient layer, excitatory neurons not only receive excitatory
inputs from thalamus, but also receive excitatory and inhibitory inputs from local excitatory
and inhibitory neurons respectively. My graduate work has been focusing on the following
questions. How does the thalamic and cortical excitation contribute to visual signal processing
in layer 4? How does the cortical inhibition integrate with excitation to affect the spike output
in layer 4 and layer 2/3? Which subtype of inhibitory neurons provide the major synaptic
inhibition?
To address these questions, I used mouse primary visual cortex (V1) as a model. On one hand,
elongated spatial receptive field, orientation selectivity and direction selectivity first emerge
in V1. Thus a fundamental and important question is how these distinct visual properties are
determined by different synaptic inputs? On the other hand, recent studies showed feature
2

selectivity in mouse V1 is similar to that in other species (Niell and Stryker, 2008; Liu et al.,
2009; Ma et al., 2010). Take advantage of powerful genetic tools, mouse is becoming a great
animal model to provide causal link for dissecting neural circuits.
Methodology
A neuron receives and integrates synaptic excitatory and inhibitory inputs, and sends out spikes
if its membrane potential passes the spike threshold. To dissect the synaptic circuits underlying
visual processing, we applied in vivo electrophysiology to record the inputs and outputs of a
neuron. The basic hypothesis is that the dynamics and properties of a neuron evoked by a
certain visual stimulus is repeatable at different time.
Figure 1.1 Illustration of in vivo electrophysiology. A neuron (red triangle) receives synaptic excitation (red
arrow) and inhibition (blue arrow), integrates the inputs to affect the membrane potential output (green arrow),
and trigger action potential output (brown arrow) one it passes the spike threshold. The synaptic inputs could be
revealed by whole-cell voltage clamp, and the membrane potential and spike output could be revealed by whole-
cell current clamp and loose patch clamp respectively.
First, we apply whole-cell voltage-clamp technique (Borg-Graham et al., 1998; Wehr and
Zador, 2003; Zhang et al., 2003; Wu et al., 2006; Liu et al., 2010; Li et al., 2012b) to reveal
the excitatory and inhibitory conductance. By clamping the cell at –70 mV, which is the
reversal potential of GABAA gated chloride channel, we can record the pure excitatory
Excitation Inhibition
Spike response
Vm response
Spike threshold
Whole-cell voltage clamp
–70 mV 0 mV
Whole-cell current clamp
Loose patch clamp
3

currents. By clamping the cell at 0 mV, which is the reversal potential of glutamate gated cation
channel, we can record the pure inhibitory currents. Then the synaptic conductance can be
derived accordingly. Second, we apply whole-cell current-clamp technique (Ferster, 1986;
Douglas et al., 1988) to reveal the membrane potential responses. Third, we apply loose-patch
clamp technique to record the spike responses. In this way, we could investigate how the
excitatory and inhibitory inputs integrate to affect the membrane potential and spike outputs.
We also combined in vivo whole-cell voltage-clamp technique with optogenetic tool to probe
the roles different excitatory inputs (Li et al., 2013). In V1 layer 4, we silenced the intracortical
excitatory circuitry in a reversible way, and recorded the thalamic input and total input in the
same neuron. Thus we could explore the relative contributions of thalamic excitation and
cortical excitation to visual processing.
Visual system
1.2.1 Receptive field
The receptive field is a sensory space that can elicit neuronal responses when stimulated. The
sensory space can be defined in different dimensions. For example, odor space is one
dimension, and visual space is two dimension. The neuronal responses can be defined as spike
responses, membrane potential responses or synaptic responses. In visual system, the receptive
field could be spatial receptive field, temporal receptive field or spatial-temporal receptive field
(Alonso and Chen, 2009).

4

1.2.2 Orientation and direction selectivity
Given different visual stimuli, some neurons respond preferentially to one specific stimulus.
This functional property is called feature selectivity. Two important feature selectivity are
orientation selectivity and direction selectivity. Orientation selective neurons respond better to
one or a few orientations, and orientation selectivity is the building block for perceiving objects
with different shapes and forms. Direction selective neurons respond better to one or a few
directions, and direction selectivity is neural basis for detecting moving directions. The feature
selectivity is largely determined by the spatial temporal receptive field structure (Lampl et al.,
2001; Priebe and Ferster, 2005, 2008).
Visual pathway
In visual system, the energy of light is converted to electrical signal in retina by photoreceptors
(Figure 1.1). The electrical signal is sent to dorsal lateral geniculate nucleus (dLGN) in
thalamus and then to visual cortex (Hubel, 1995).
Retina
Visual
cortex
dLGN
Figure 1.2 Visual pathway (Modified from Hubel, 1995)
5

In the eye, light is focused by the cornea and the lens before reaching the retina. Retina contains
three major functional classes of neurons: photoreceptors, interneurons and ganglion cells.
There are two types of photoreceptors, rods and cones. Rods detect dim light and mediate night
vision. Cones meditate color vision and is responsible for day vision. Interneurons contain
bipolar cells, horizontal cells and amacrine cells. Bipolar cells have a center-surround receptive
field, and have two types, ON-center and OFF-center bipolar cells. The type is determined by
the activation of cation channels by glutamate from the photoreceptors. Different bipolar cells
may connect with each other via horizontal cells. Ganglion cells receive input from bipolar cell
either directly or via amacrine cells. Ganglion cells also have a center-surround receptive field,
which can be ON-center or OFF-center. Most ganglion cells fall into two functional classes,
M (large cell) or P (small cells), which are the beginning of M pathway and P pathway. In the
visual pathway, retina is the first stage performing neural computation, and ganglion cells are
specialized for the detection of contrasts of visual image (Tessier-Lavigne, 2000).
From the retina, optic nerves carry the information to three major subcortical targets: the
pretectum, the superior colliculus and the dLGN. Pretectum controls pupillary reflexes.
Superior colliculus controls saccadic eye movement. DLGN is the principle structure that
provide input to the visual cortex. DLGN contains six layers. Layers 1 and 2 receive inputs
from M ganglion cells, are called magnocellular layers. Layers 3, 4, 5 and 6 receive inputs
from P ganglion cells, are called parvocellular layers. Each dLGN neurons receive inputs only
from a very few ganglion cell axons, which attribute the similar visual properties to dLGN
neurons as ganglion cells (Wurtz and Kandel, 2000a).
6

From dLGN, visual information goes to visual cortex, which also contains six layers. In
primate, layer 4C of primary visual cortex (V1) is the major layer receiving direct inputs from
dLGN. M pathway project to layer 4C and P pathway project to layer 4C . Different from
neurons in the retina and dLGN, most V1 neurons respond best to linear stimuli with certain
orientations. The majority of orientation selective neurons belongs to simple cells or complex
cells. Simple cells have separated ON and OFF subregions, and complex cells have overlapped
ON and OFF subregions. In primate V1, neurons are organized in functional column, such as
orientation column and ocular dominance column. Columns of neurons with similar function
are linked through horizontal connections (Wurtz and Kandel, 2000a). From V1, the M and P
pathways feed into two cortical pathways: a dorsal pathway and a ventral pathway. The dorsal
pathway to the posterior parietal cortex primarily mediate the perception of motion and depth.
The ventral pathway to the inferior temporal cortex largely mediate the perception of contrasts
and contours (Wurtz and Kandel, 2000b).
Organization
Chapter 2 to Chapter 5 focus on the synaptic circuits in layer 4 of V1. In Chapter 2, we
investigate the relative roles of thalamic excitation and cortical excitation in processing visual
information. Specifically we address the following questions. How are the orientation
selectivity, direction selectivity and spatial receptive field of total excitatory inputs determined
by the thalamic excitation and cortical excitation? Can the orientation selectivity of thalamic
excitation be predicted from thalamic receptive field? In Chapter 3, we study the synaptic
circuits for orientation selectivity at different visual contrasts. We are interested in the
following questions. How is the synaptic inputs modulated by different contrasts? How does
7

the modulation of synaptic inputs affect the selectivity of spike output? Which subtype of
inhibitory neuron is mainly involved in the modulation of synaptic inhibition? Chapter 4
focuses the synaptic circuits underlying direction selectivity in simple cell with one dominant
subregion. We answer the following questions. What’s the synaptic inputs of a direction
selective neuron? How do excitation and inhibition interplay to affect the direction selectivity
of spike output? Can the direction selectivity of output be explained by the spatial temporal
receptive field? In Chapter 5, we explore the synaptic circuits for orientation selectivity during
development, and address the following questions. How does the orientation selectivity of
spike responses develop? What synaptic mechanisms can account for this development? Which
subtype of inhibitory neuron is involved in the mechanisms? In Chapter 6, we probes the
synaptic circuits underlying the difference of orientation selectivity between simple and
complex cells in layer 2/3 of V1 (collaborated with Dr. BaoHua Liu). We specifically address
the following questions. What are the synaptic properties for orientation selectivity in simple
and complex cells? Which synaptic input determine the different selectivity in different cell
type? Can the synaptic orientation selectivity be predicted from receptive field structure?

8

Chapter 2: Relative Roles of Thalamic Excitation and
Cortical Excitation in Processing Visual Information
Introduction
Neurons in layer 4 of the V1 receive excitatory inputs from two major sources: the feedforward
thalamocortical input and the intracortical input from other cortical neurons (Douglas and
Martin, 1991; Callaway, 1998). Since the proposal that a linear spatial arrangement of thalamic
neuron receptive fields results in orientation-tuned input to simple cells (Hubel and Wiesel,
1962; Reid and Alonso, 1995; Lampl et al., 2001), the respective roles of thalamocortical and
intracortical inputs in generating cortical orientation selectivity have been intensively studied
(Ferster and Miller, 2000). In one view, the feedforward input is sufficient for generating sharp
orientation selectivity (Ferster et al., 1996; Chung and Ferster, 1998). In a second view, the
feedforward input only provides a weak orientation bias, and orientation selectivity is greatly
strengthened by excitation (e.g. recurrent excitation) from other cortical neurons tuned to the
same orientation (Ben-Yishai et al., 1995; Douglas et al., 1995; Somers et al., 1995; Ben-Yishai
et al., 1997; Adorjan et al., 1999; McLaughlin et al., 2000).
Previously, several experimental methods have been used to silence cortical spikes and isolate
thalamocortical input: 1) pharmacological silencing of the cortex by activating GABAA
receptors with muscimol (Liu et al., 2007; Khibnik et al., 2010); 2) cooling of the cortex
(Ferster et al., 1996); 3) electrical shocks in the cortex to produce an inhibitory widow of
hundreds of milliseconds during which spikes cannot be generated (Chung and Ferster, 1998).
9

Results from these previous studies in general agree with the notion that neurons in layer 4
inherit their functional properties from the relay of thalamic inputs. However, due to the
technical limitations in previous methods, e.g. the non-specific effects on synaptic transmission
(Yamauchi et al., 2000; Porter and Nieves, 2004) or difficulties of reversible applications (Liu
et al., 2007), the precise contributions of thalamocortical and in particular intracortical circuits
to cortical orientation selectivity and other functional properties remain to be determined.
Optogenetic approaches (Zhang et al., 2007; Bernstein et al., 2012) provide an unprecedented
advantage in addressing this question, since specific activation of parvalbumin positive (PV )
inhibitory neurons alone can effectively and reversibly silence spiking of cortical excitatory
neurons (Olsen et al., 2012). In this study, we combined in vivo whole-cell voltage-clamp
recordings with optical activation of PV  inhibitory neurons to isolate thalamocortical
excitation from the total excitation in the same neuron. Our results indicated that intracortical
excitatory circuits preserved the orientation and direction tuning of feedforward input by
linearly amplifying its signals, and expanded the spatial visual receptive field by recruiting
more distant inputs possibly via horizontal circuits.
Methods
All experimental procedures used in this study were approved by the Animal Care and Use
Committee of USC.
2.2.1 Viral injection
Female mice (45-60 days) used in experiments were generated by crossing PV-Cre mice with
tdTomato reporter mice (The Jackson Laboratory, C57BL/6J background). We anaesthetized
10

mice with 2% isoflurane, thinned the skull over V1 and performed ~0.2 mm
2
craniotomy. We
delivered the virus using a bevelled glass micropipette (tip diameter 40 – 50 m) attached to a
microsyringe pump (World Precision Instruments). Adeno-associated viruses (AAVs) to
deliver channelrhodopsin-2 (ChR2) were acquired from the UPenn Viral Vector Core:
AAV2/9.EF1α.DIO.hChR2 (H134R)-EYFP.WPRE.hGH (Addgene 20298). We injected virus
at a volume of 50 nl/injection and at a rate of 20 nl/min. We performed the injection at two
locations ([0.8, 2.3], [0.8, 3] mm anterior and lateral to lambda) and two depths (300 m and
600 m). We then sutured the scalp, and administered an analgesic (0.1 mg/kg Buprenex) to
help the recovery from anaesthesia. We made in vivo recordings 2–3 weeks after viral
injections. We examined the expression pattern of hChR2 (H134R)-EYFP in each injected
mouse before the experiment, and carried out recordings only in animals with a correct location
of EYFP expression (1 out of 20 mice was excluded). That is, for the major experiments, there
was only one animal group. In more than 300 EYFP-expressing neurons examined in 5 mice,
all of them expressed tdTomato, indicating that they were all PV  inhibitory neurons.
2.2.2 Animal preparation
We sedated the mouse with EYFP expression with an intramuscular injection of
chlorprothixene hydrochloride (10 mg/kg in 4 mg/ml water solution) and then anesthetized it
with urethane (1.2 g/kg, i.p., at 20 w/v in saline), as previously described (Niell and Stryker,
2008; Liu et al., 2009). We maintained the animal’s body temperature at ~37.5  by a heating
pad (Havard Apparatus, MA). We performed tracheotomy, and inserted a small glass capillary
tube to maintain a free airway. We performed cerebrospinal fluid draining, removed the skull
and dura mater (~ 1  1 mm) over the V1, and applied artificial cerebrospinal fluid solution
11

(ACSF, containing [in mM] 140 NaCl, 2.5 KCl, 2.5 CaCl2, 1.3 MgSO4, 1.0 NaH2PO4, 20
HEPES, 11 glucose, pH 7.4) to the exposed cortical surface when necessary. We trimmed
eyelashes contralateral to the recording side, and covered the eyes with ophthalmic lubricant
ointment until recording, at which time we rinsed the eyes with saline and applied a thin layer
of silicone oil (30,000 centistokes) to prevent drying while allowing clear optical transmission.
Eye movements and the receptive field drift were negligible within the time window of our
recordings (Mangini and Pearlman, 1980; Gentet et al., 2000; Liu et al., 2010; Niell and Stryker,
2010).
2.2.3 In vivo electrophysiology
We pre-penetrated the pia with a broken pipette under visual guidance before in vivo
recordings, and then performed whole-cell voltage-clamp recordings with an Axopatch 200B
(Molecular Devices). The patch pipette had a tip opening of ~2 m (4 – 5 M  impedance).
The Cs

-based intrapipette solution used for voltage-clamp recordings contained (in mM): 125
Cs-gluconate, 5 TEA-Cl, 4 MgATP, 0.3 GTP, 8 disodium phosphocreatine, 10 HEPES, 10
EGTA, 2 CsCl, 1 QX-314, 0.75 MK-801, biocytin 1 , pH 7.25.
The K

-based intrapipette solution used for sequential cell-attached and whole-cell recordings
contained (in mM): 130 K-gluconate, 4 MgATP, 0.3 GTP, 8 disodium phosphocreatine, 10
HEPES, 10 EGTA, 2 KCl, biocytin 1 , pH 7.25. The pipette capacitance and whole-cell
capacitance were compensated completely, and series resistance was compensated by 50% –
60% (at 100 s lag). A –11 mV junction potential was corrected. Signals were filtered at 2 kHz
and sampled at 10 kHz. We isolated excitatory currents by clamping the cell at the reversal
12

potential for LED-evoked Cl
–
currents (–64  6 mV), which was determined for each individual
experiment. As discussed previously (Liu et al., 2010), our whole-cell recording method with
relatively large pipettes highly biases sampling towards pyramidal neurons. For cell-attached
recordings only, the pipette contained ACSF, and we recorded spikes in the voltage-clamp
mode, with a small commend potential applied to achieve a zero baseline current. The spike
signal was filtered at 10 kHz and sampled at 20 kHz. The spike waveform of recorded
excitatory neurons had a trough-to-peak interval of 0.85  0.10 ms (n  35 cells). We recorded
the extracellular ensemble currents with a patch pipette filled with 1M NaCl, under voltage
clamp with a holding voltage of 0 mV. Signals were filtered at 10 kHz and sampled at 20 kHz.
We determined the depth location of layer 4 (370 m to 510 m from the pia) based on the
expression pattern in a layer-4-specific Cre line (Scnn1a-Tg3-Cre, the Jackson laboratory)
crossed with the tdTomato reporter line. The layer assignment of the blindly recorded neurons
was made mostly according to the vertical travel distance of the electrode. The assignment was
reasonably precise because our use of a high-magnification objective (40 ) on the microscope
allowed a precise identification of the cortical surface and our application of pre-penetration
minimized the dimpling of the cortical surface. Morphologies of 15 recorded layer 4 cells were
successfully reconstructed (see Figure 1d), which confirmed that they were located in layer 4.
For recording in the dLGN, we made a square craniotomy of 1.5 mm  1.5 mm approximately
2.5 mm posterior and 2 mm lateral to the bregma structure. We applied cell-attached recordings
to collect spikes from single neurons. The spike signal was filtered at 10 kHz and sampled at
20 kHz. We recorded from dLGN relay neurons, characterized by robust visually evoked
responses with low spontaneous activity, at a depth of 2500 – 3100 m (Grubb and Thompson,
13

2003). For recording in the thalamic reticular nucleus (TRN), we made a square craniotomy of
1.5 mm  1.5 mm around 1.1 mm posterior and 1.6 mm lateral to the bregma structure, and
carried out cell-attached recordings at a depth of 2400 – 3000 m.
2.2.4 In vivo two-photon imaging guided recording
We tuned a mode-locked Ti:sapphire laser (MaiTai Broadband, Spectra-Physics, Mountain
View, CA) at 890 nm with the output power at 60 – 300 mW for imaging fluorescently labeled
neurons in layer 4, and adjusted the power according to the cell’s fluorescence level. We filled
the glass electrode, with 1 m tip opening and 8 – 10 M  impedance, with ACSF containing
0.15 mM calcein (Invitrogen). We completely compensated the pipette capacitance. We
navigated the pipette tip in the cortex and patched it onto a fluorescent soma as previously
described (Liu et al., 2009). After confirming a successful targeting, we released the positive
pressure in the pipette ( 10 mbar) and applied a negative pressure (20 – 150 mbar) to form a
loose seal (with 80 – 200 M  resistance). We directly determined the depth of the patched cell
under the two-photon microscope. The depth of the recorded PV+ neurons ranged from 365 –
455 m below the pia. The recorded PV+ neurons all exhibited narrow spike waveforms, with
an average through-to-peak interval of 0.32 0.05 ms (n  6).
2.2.5 Visual stimulation
We implemented the visual stimuli using Matlab with Psychophysics Toolbox and displayed
them with a gamma-corrected LCD monitor (refresh rate 75 Hz, maximum luminance 280
cd/m
2
) placed 0.25 m away from the right eye. We placed the center of the monitor at 45 
Azimuth, 25 Elevation, and it covered 35  horizontally and 27 vertically of the mouse
14

visual field. We made recordings in the monocular zone of the V1. We recorded spontaneous
activity by applying a uniform grey background (luminance: 41.1 cd/m
2
). To measure
orientation tuning, we applied drifting single light bars (57.5 cd/m
2
, 4  60, at a speed of
50 /s) or dark bars (24.7 cd/m
2
) of 12 directions (30  step) in a pseudorandom sequence. The
visual stimulation with and without LED illumination were alternated, but the stimulus
sequence was randomized independently for LED off and LED on trials. Therefore, data
collection was randomized. We set the inter-stimulus interval at 10 s to allow a full recovery
of ChR2 function from desensitization (Lin et al., 2009). We applied five to ten sets of stimuli
to each cell, with the sequence different between sets. For recordings in the dLGN and TRN,
we applied both drifting bars and full-field drifting sinusoidal gratings (temporal frequency 
2 Hz, spatial frequency  0.04 cycles/degree, 95% contrast) at 12 directions. We also mapped
the receptive field with flash stimuli, either flash light squares (5   5 ) or flash light bars (4 
 60 ) of vertical orientation for 5 – 10 repetitions in a pseudorandom sequence.
2.2.6 Photostimulation
To photoactivate ChR2, we used a blue (470 nm) fiber-coupled LED (0.2 mm diameter, Doric
Lenses) placed on top of the exposed cortical surface. LED light spanned the entire area of V1.
We applied black pigment stained agar to prevent LED light from scattering and reaching the
contralateral eye, and had verified that LED light did not directly stimulate the eye in wild-
type mice. The LED was driven by the analog output from a NIDAQ board (National
Instruments). The intensity of LED was around 5 mW (measured at the tip of the fiber).

15

2.2.7 Data analysis
We performed data analysis with custom-developed software (LabVIEW, National Instrument;
and MATLAB, Mathworks), not blind to the conditions of the experiments. We counted the
spikes evoked by drifting bars or drifting sinusoidal gratings within a time window covering
the visual stimulation duration with a 70 ms delay, and subtracted the spontaneous firing rate
from the stimulus-evoked spike rate. We averaged the recorded synaptic responses, and
smoothed it by averaging within a sliding 40 ms window (Li et al., 2012a).
We quantified the strength of orientation selectivity with a global orientation selectivity index
(gOSI):
 
  ) ( / ) (
2
 

R e R gOSI
i

i is 1  .  is the angle of the moving direction. R( ) is the response level at angle . We
averaged the response levels of two directions at the same orientation to obtain the orientation
tuning curve between 0 – 180 degrees, and fitted it with a Gaussian function R( )  A  exp(–
0.5  ( – )
2
 
2
)  B.  is the preferred orientation.  is the tuning width. To measure the
direction selectivity index (DSI), we fitted the response levels at twelve stimulus directions to
a wrapped Gaussian function R( )  A1  exp(–0.5  (  – )
2
/ 
2
)  A2  exp(–0.5  ( – –
180º )
2
/ 
2
)  B.  is the preferred direction.  is the tuning width. DSI was defined as (A1 –
A2) / (A1  A2  2B).
LED illumination alone led to a decrease in input resistance (from 181  22 to 118  24 M ,
P  0.002, one-tailed paired t-test, n  5 cells from 5 mice), which was measured by examining
16

the voltage change to a 100 pA step current. We estimated how much the decrease of input
resistance would affect the recorded current amplitude based on the following equation (Liu et
al., 2007):
syn
s in
in
rec
I
R R
R
I 



Isyn is the actual amplitude of synaptic current. Irec is the recorded amplitude. Rin is the input
resistance. Rs is the effective series resistance (15~30 M ) in our recordings, which was
unchanged after cortical silencing). The decrease of Rin from 181 to 118 MΩ would lead to a
4% ~ 7% reduction of the recorded synaptic amplitude, which is negligible compared to the
measured amplitude reduction after cortical silencing (Figure 2.2F). It should be noted that
the putative change in recorded current amplitudes due to the change in input resistance would
not significantly affect the tuning of synaptic responses. Similarly as we have previously
discussed (Wu et al., 2006; Liu et al., 2007; Liu et al., 2010), under our recording condition,
the observed synaptic responses can be reasonably controlled by the somatic voltage clamp.
This was suggested by the linear I V relationship and the proximity of LED-evoked currents
to the expected reversal potential of inhibitory currents (Figure 2.1D). The thalamocortical
synapses on layer 4 neurons have been shown to be proximal to the soma (Petreanu et al.,
2009). These synaptic inputs would be less affected by changes in input resistance compared
to inputs onto distal dendrites (Zhang et al., 2011). Nevertheless, potential deviations of
measured synaptic amplitudes from bona fide amplitudes caused by space-clamp errors and
cable attenuation should be recognized (Wehr and Zador, 2003; Tan et al., 2004; Wu et al.,
2011).
17

To derive receptive field boundaries, we translated the onset delay of each drifting-bar
response (after compensation for the subcortical conduction delay as determined from the
cell’s response to flash noise stimuli, see Figure 2.4B) into the distance the bar had moved. To
determine the response onset, we first identified the time point where the peak current occurred,
and then traced current backward from the peak time to the time point where the amplitude
was reduced to 5% of the peak value. We also visually examined response traces to confirm
the determined onsets. The lines marking the bar positions at the compensated response onsets
intercepted to form a dodecagon that outlined the spatial receptive field. We determined the
midpoint of each side of the dodecagon, and performed the least squares fitting to an ellipse
for the twelve midpoints. We defined the receptive field size as the length of the major axis of
the ellipse, and the aspect ratio as the ratio of the major versus minor axis of the ellipse.
For flash stimuli, we identified the visually evoked responses if the average peak current was
3 standard deviations greater than the baseline current in the absence of visual stimuli. For
synaptic responses to flash squares, we fitted the receptive field to an elliptic function, and
determined the receptive field boundary as previously described (Liu et al., 2010).

Results
2.3.1 Optogenetic silencing of visual cortical circuits
For optogenetic silencing, we utilized the Cre/loxP recombination to express ChR2 in PV+
inhibitory neurons (see Methods). We injected an adeno-associated viral vector AAV2/9-
EF1 -DIO-ChR2-EYFP into the V1 of PV-Cre tdTomato mice. As shown by the EYFP
fluorescence in cortical slices from animals two weeks after the injection, ChR2 was expressed
18

across cortical layers (Figure 2.1A, top) and specifically in PV  neurons (Figure 2.1A,
bottom). We applied illumination of the exposed visual cortical surface with blue LED light
(470 nm) via an optical fiber. In the V1 region expressing EYFP, we carried out in vivo cell-
attached recordings from excitatory neurons to examine the effects of optical activation of PV+
neurons. We found that LED illumination resulted in complete silencing of visually evoked
spikes shortly after its onset, and that the effect sustained throughout the duration of the
illumination (Figure 2.1B, left). We observed such silencing effect throughout layer 4–6
(Figure 2.1B, right). To confirm that the silencing effect was through activating PV 
inhibitory neurons, we recorded from PV  neurons under visual guidance aided by two-photon
imaging (Liu et al., 2009; Ma et al., 2010) (see Methods). We found that opposite to the effect
on excitatory neurons, LED illumination dramatically increased the firing rate of PV+ neurons
(Figure 2.1C). Although adapted slightly, the high firing rate could be maintained throughout
the duration of LED illumination for at least a few seconds (Figure 2.1C, left). Furthermore,
whole-cell voltage-clamp recordings from excitatory neurons revealed that LED illumination
alone induced a large sustained current, the reversal potential of which was consistent with that
of Clˉ currents (Figure 2.1D). These experiments demonstrated that optogenetic activation of
PV  inhibitory neurons effectively silenced spiking of cortical excitatory neurons and thus
eliminated intracortical connections.
Previous studies in auditory and visual cortices have suggested that thalamocortical axon
terminals contain GABAB receptors (Yamauchi et al., 2000; Porter and Nieves, 2004).
Activation of these presynaptic receptors by GABA agonists such as muscimol can reduce
transmitter release (Liu et al., 2007; Khibnik et al., 2010). We thus examined whether
19

optogenetic activation of PV  neurons could potentially lead to a reduction of thalamocortical
transmission caused by a spillover of GABA released from inhibitory synapses made by PV 
cells. We recorded extracellular ensemble currents evoked by flash noise stimuli in layer 4 (see
Methods), which reflect the summed neuronal and synaptic activity within a local cortical area
B A C
V1 V1
20 µm
500 µm
D E F
L4
L2/3
r = 0.99
0.2 s
2 nA
100 µm
Current (nA)
Voltage (mV)
–80 –60 –40 –20 0
0.0
0.3
0.6
0.5 s
0.1 nA
VEC_ LED off (nA)
VEC_LED on (nA)
0.0 0.2 0.4
0.4
0.2
0.0
0.2 nA
0.5 s
Exc_LED on (nA)
Exc_LED off (nA)
0.0
0.1
0.2
0.2 0.1 0.0

0
4

Spike number
Time (s)
1 ms
20 µm
0 1 2
LED
off
LED
on
Firing rate (Hz)
80
40
0
3
0
4

Firing rate (Hz)
LED
off
LED
on
L5
L6

Time (s)
L4
1 ms
20
10
0
20
10
0
10
5
0
1
3 0
0
1
0
Spike number
–80 mV
–70 mV
–10 mV
–50 mV
Figure 2.1 Optogenetic silencing of visual cortical circuits. A, Top, confocal images showing tdTomato (red)
and ChR2-EYFP expression (green) patterns. Bottom, enlarged images. B, Left, peri-stimulus spike time
histograms (PSTHs) for responses of a layer 4 excitatory neuron to a flash noise stimulus (red bar) with and
without LED illumination (blue bar). Top, visual stimulus pattern and superimposed 50 individual spikes. Right,
average firing rates in LED off and LED on trials for cells in different layers (n = 14, 10 and 11 cells from L4, L5
and L6 from 6, 5 and 5 mice, respectively). C, Left, PSTHs for responses of a tdTomato-labeled PV+ neuron. Top
inset, two-photon image of the recorded cell and superimposed 100 individual spikes. Right, average firing rates
for 6 PV+ cells from 6 mice. D, Top, LED illumination–induced currents in a cell and its reconstructed
morphology. Bottom, current amplitude (averaged in a 40-ms window) versus holding voltage (one sided, P =
0.005). E, Top, visually evoked ensemble currents (VECs) recorded in layer 4 without (left) and with (right)
preceding LED illumination. Inset, superimposed traces. Bottom, peak amplitudes in LED on versus LED off
trials (0.26 ± 0.11 versus 0.24 ± 0.08 nA, P = 0.07, two-tailed paired t test, n = 8 sites from 8 mice). F, Top,
visually evoked excitatory currents without and with a preceding LED illumination. Bottom, peak amplitudes in
LED on versus LED off trials (median = 0.051 versus 0.051 nA, P = 0.23, two-sided Wilcoxon signed-rank test,
n = 10 cells from 10 mice).
20

(Katzner et al., 2009). We then delivered LED light immediately before the visual stimulus. If
there is a reduction of presynaptic release, we would expect to see a decrease in the visually
evoked ensemble current. This effect is also expected to last for 1–2 seconds since the decay
time constant for GABAB receptors is 2.8 s (Pfrieger et al., 1994). We found that LED
illumination directly induced a negative ensemble current (Figure 2.1E, top). Nevertheless,
the amplitude of the following visually evoked current was not apparently reduced (Figure
2.1E, bottom), neither was its temporal profile altered (Figure 2.1E, top). Additionally, we
examined visually evoked excitatory currents in layer 4 neurons, applying similar visual
stimulation without and with coupling LED illumination (Figure 2.1F, top). Again, we did not
observe a reduction of the visually evoked excitatory currents in individual cortical cells
(Figure 2.1F, bottom). Altogether, these control experiments suggested that there was no
presynaptic inhibition caused by LED-induced GABA release, possibly because GABAergic
synapses made by PV  neurons are relatively distant from thalamocortical synapses. Thus,
the optogenetic activation of PV+ neurons could be an effective method to silence the cortex
without significantly affecting thalamocortical transmission.
2.3.2 Scaling of orientation-tuned thalamocortical input
We next examined excitatory synaptic responses to single drifting bars at 12 different
directions without and with coupling LED illumination. We carried out in vivo whole-cell
voltage-clamp recordings with a Cs

-based internal solution from layer 4 excitatory neurons
and clamped the cells at the reversal potential for inhibitory currents, which was determined
21

from LED-evoked currents (see Figure 2.1D). We interleaved control trials with visual
stimulus only and trials with PV  neuron photostimulated. As shown in an example cell, LED
illumination reduced the amplitude of excitatory currents to all directions of bar movement
A
LED off LED on
B
C F
0 0.5 1.0
40
20
0
Scaling factor
Percentage of cells
Norm. amp. Current amp. (nA)
Total
0.2
0.1
0
0.06
Thal.

0.03
0
–30
( )
1.0
0.5
0
–120
60
Thal (nA)
Total (nA)
0.1 0 0.2
k = 0.25
r = 0.95
0.04
0.02
0

( )
Norm. amp.
Total
Thal.
–90 0 90
0
0.5
1.0
D
0
0.05
0.10
0.15
gOSI_Total
gOSI_Thal.
0 0.05 0.10 0.15
E
Pref. _ Total ( )
Pref. _ Thal. ( )
0 90
180
0
90
180
0.06 nA
0.03 nA
0.08 nA
0.14 nA
0.1 nA
0.2 nA
90 
270 
0  180 
Figure 2.2 Linear amplification of orientation-tuned thalamocortical input. A, Left, average excitatory
responses (five trials) of a cell to single drifting bars at 12 different directions. Arrowheads mark the preferred
orientation. Red and blue dashed curves mark the response onsets. Scale bars represent 0.1 (red) and 0.04 (blue)
nA, and 0.5 s. Top right, orientation tuning curves of peak current amplitude for the total and thalamocortical
(Thal.) excitation, as well as superimposed normalized tuning curves. Error bars represent SD Bottom right, peak
current amplitudes at six orientations of LED on versus LED off trials. Dashed line indicates the linear fitting: k
is the slope, r is the correlation coefficient, one-sided P = 0.0009. B, Polar plots of excitatory current amplitude
before (red) and after (blue) silencing the cortex for another three cells. The maximum axis value is labeled. C,
Average normalized orientation tuning curves of total excitatory input (red) and of thalamocortical input (blue).
Error bars represent SEM. N = 19 cells from 19 mice. D, OSI of thalamocortical input versus that of total
excitation (0.059 ± 0.021 versus 0.056 ± 0.023, P = 0.4, two-tailed paired t test, n = 19 cells). Light gray triangles
represent individual cells that deviated significantly from the identity line (P < 0.05, bootstrap analysis). E,
Preferred orientation of thalamocortical input versus that of total excitation (P = 0.6, two-tailed paired t test, n =
19 cells). F, Distribution of scaling factors in the recorded cell population. The arrow indicates the mean value.
22

(Figure 2.2A, left). In addition to the change in amplitude, we observed that the response onset
latencies were prolonged (Figure 2.2A, left, dotted curves). To quantify orientation tuning, we
measured peak current amplitudes after smoothing the current traces with a 40 ms sliding
window for averaging. Despite the general reduction in amplitude after cortical silencing, there
was little change in orientation tuning of excitatory input, as shown by the normalized tuning
curves (Figure 2.2A, right). This result suggested that the excitatory responses were reduced
by a similar fraction across orientations. In another word, tuning curve was scaled down. We
quantified the scaling factor from the slope of linear fitting of response amplitudes in LED on
versus control trials (Figure 2.2A, right, bottom). As shown by the example cell, the data were
well fitted by a linear relationship, and the scaling factor was well below 1 (Figure 2.2A, right,
bottom). We showed polar graph plots of orientation tuning of excitatory currents for more
example cells (Figure 2.2B). In general, tuning shapes looked similar without and with LED
illumination, with response amplitudes clearly reduced.
We averaged the normalized excitatory tuning curves of all the recorded cells (19 from 19
mice). This “population” tuning curve was largely unchanged after cortical silencing (Figure
2.2C), supporting the notion of scaling. It is worth noting that the isolated thalamocortical input
(as well as the total excitatory input) was weakly tuned, with only a small difference between
the responses to the preferred and orthogonal orientations (Figure 2.2C). To examine the
change of tuning for each individual cell, we calculated the gOSI (see Methods). We found
that orientation tuning of excitatory input was not significantly changed after cortical silencing
in all individual cells except two (Figure 2.2D). Neither was the preferred orientation changed
in individual cells (Figure 2.2E). The slope of linear regression (i.e. scaling factor) ranged
23

from 0.19 to 0.71, with the mean of 0.38 (Figure 2.2F). This indicated that thalamocortical
input was about one third of the total excitatory input. In another word, there was a threefold
amplification of thalamocortical signals by intracortical excitatory circuits. Measurements of
integrated charge of synaptic currents also supported the notion that the tuning sharpness as
well as the preferred orientation was preserved after silencing the cortex (Figure 2.3), although
the tuning of integrated charge was weaker than that measured with peak amplitude (P  0.018,
one-tailed paired t-test, n 19 cells from 19 mice, comparison was made for responses in
control trials).
Under our current recording conditions, the linear I V relationship and the proximity of the
derived reversal potential of LED-evoked currents to the expected reversal potential of
inhibitory currents (Figure 2.1D) suggested that the somatic voltage clamp was adequate.
Therefore synaptic inputs relatively close to the soma might be reasonably well clamped. The
gOSI_Total
gOSI_Thal.
Pref. _Total ( )
Pref. _Thal. ( )
A B
0 0.05 0.10
0
0.05
0.10
0
90
180
0 90 180
Figure 2.3 Summary of orientation tuning based on measurements of integrated charge of excitatory
currents. A, Plot of gOSI of thalamocortical input versus that of total excitatory input (0.038  0.018 vs 0.040
 0.017, P = 0.38, two-tailed paired t-test, n = 19 cells from 19 mice). Dash line is the identity line. B, Plot of
preferred orientation of thalamocortical input versus that of total excitatory input (P = 0.57, two-tailed paired t-
test, n = 19 cells from 19 mice). Dash line is the identity line.
24

thalamocortical input to layer 4 neurons, synapses of which are located proximal to the soma,
is expected to be better clamped and less affected by space-clamp errors and cable attenuation
compared to inputs onto distal dendrites (also see the discussion in Methods). Nevertheless,
we recognize that there are potential deviations of measured synaptic amplitude from the bona
fide amplitude caused by space-clamp errors and cable attenuation, which need to be
G
A B C
D
E
–180 0 180
0
2
4
Current Amp. (nA)
 ( )
Spike number
 ( )
–180 0 180
0
0.04
0.08
0.12
Pref. φ_Exc (º )
Pref. _spike ( )
DSI_Exc
DSI_spike
r = 0.84
k = 0.26
360
0
180 0
0
0.25
0.50
0.50 0.25
360
180
0
 ( )
Norm. Amp.
Total
Thal.
–180 0 180
0
0.5
1.0
F
DSI_ Total
0.1 0 0.2
DSI_Thal.
0
0.1
0.2
Pref. _Total ( )
360 180 0
Pref. _Thal. ( )
0
180
360
Figure 2.4 Intracortical excitation preserves direction tuning. A, PSTH for spike responses (left, ten trials)
to single drifting bars of an example layer 4 cell as well as its average excitatory responses (right, ten trials)
recorded under voltage clamp. Scale bars represent 30 Hz (left) and 0.1 nA (right), 0.5 s. B, Top, tuning curve of
average spike count (ten trials) for the same cell. Bottom, tuning curve of average peak excitatory current. Error
bars represent SD C, DSI of excitatory input versus that of spike response (n = 20 cells from 20 mice). Linear
fitting (olive dashed line): one-sided P = 10−5. D, Preferred direction of excitatory input versus that of spike
response for cells with DSI > 0.2 (P = 0.3, two-tailed paired t test, n = 13 cells from 13 mice). E, Average
normalized direction tuning curves for total excitation (red) and thalamocortical excitation (blue). Error bars
represent SEM. N = 19 cells from 19 mice. F, DSI for thalamocortical excitation versus that for total excitation
(0.104 ± 0.045 versus 0.102 ± 0.043, P = 0.49, two-tailed paired t test, n = 19 cells from 19 mice). G, Preferred
direction of thalamocortical excitation versus that of total excitation (P = 0.86, two-tailed paired t test, n = 19
cells).
25

investigated in the future.
2.3.3 Intracortical excitation preserves direction tuning
Layer 4 neurons exhibit not only orientation selectivity, but also direction selectivity (Niell
and Stryker, 2008; Ma et al., 2010). In order to understand the relationship between direction
selectivity of spike response and that of excitatory input, we carried out sequential cell-attached
and whole-cell recordings (with a K
+
-based internal solution) from the same neurons in wild
the direction selectivity of the cell’s output response, and the subsequent whole-cell recording
allowed us to examine the underlying excitatory drive. As shown by an example cell (Figure
2.4A, B), although the cell exhibited clearly direction-selective spike responses, the amplitude
of excitatory current only showed a slight difference between the preferred and null directions.
Thus, consistent with what has been previously reported, the spike threshold greatly amplified
the selectivity of output response (Priebe and Ferster, 2008). The plot of DSI of spike response
versus that of excitatory current revealed a strong linear relationship (Figure 2.4C). In
addition, the preferred direction of spike response was essentially the same as that of excitatory
drive (Figure 2.4D). These results indicated that the selectivity of spike response strongly
correlated with that of excitatory input, which might be employed to predict direction
selectivity of the neurons.
We next examined how direction tuning of excitatory drive is determined by thalamocortical
and intracortical inputs. We found that the direction tuning of excitatory drive was not changed
by silencing intracortical inputs, as shown by the superimposed average direction tuning curves
without and with LED illumination (Figure 2.4E). On an individual cell base, DSI of
thalamocortical excitation was also similar to that of total excitation (Figure 2.4F), and the
26

preferred direction was unchanged after silencing the cortex (Figure 2.4G). Similar
conclusions could be made when the integrated charge of excitatory current was considered
(Figure 2.5). Together these results further demonstrated a linear amplification effect of
intracortical excitatory circuits. The feedforward input to layer 4 neurons was already
direction-tuned, and the intracortical excitation increased the gain of the signal, without
affecting its tuning property.
2.3.4 Intracortical excitation expands visual receptive field
Taking advantage of drifting-bar evoked responses, we were able to estimate the shape and
size of the spatial receptive field of excitatory drive. We estimated the receptive field boundary
based on the moving bar speed and the response latency at each stimulus direction (Figure
2.6A, top left). We found that the response onset latency was prolonged in the presence of LED
illumination at all stimulus directions (Figure 2.6A, bottom), suggesting that the visual
receptive field had “shrunk” after cortical silencing. We derived receptive field outlines for the
DSI_ Total
DSI_Thal
Pref. _Total ( )
Pref. _Thal ( )
A B
0
90
180
0 90 180
0
0.05
0.15
0.10
0 0.05 0.15 0.10
Figure 2.5 Summary of direction tuning based on measurements of integrated charge of excitatory
currents. A, Plot of DSI of thalamocortical input versus that of total excitatory input (0.065  0.020 vs 0.066 
0.026, P = 0.9, two-tailed paired t-test, n = 19). Dash line is the identity line. B, Plot of preferred direction of
thalamocortical input versus that of total excitatory input (P = 0.49, two-tailed paired t-test, n = 19). Dash line is
the identity line.
27

total excitation and thalamocortical excitation respectively (see Methods) (Figure 2.6A,
bottom), and fitted it to an ellipse (Figure 2.6A, top right). We found that the derived receptive
fields were both slightly elongated, and the major axes of both receptive fields (i.e. the axis for
receptive field elongation) were similar as the preferred orientation of the cell’s excitatory
drive under moving stimuli (marked by the blue arrows in Figure 2.6A). The observation in
this example cell suggested that the size of spatial receptive field was reduced in the presence
Aspect ratio_Thal.
0 2
r = 0.71
0
0.05
0.10
0.15
Total Thal.
A B C
G E
D
F
180º boundary 90º boundary
I
Pref θ_Thal. (º )
RF size_Total (º )
RF size_Thal. (º ) RF axis_Thal. (º )
RF axis_Thal. (º ) gOSI_Thal.
Aspect ratio_Total
Aspect ratio_Thal.
RF size_Thal. (º )
RF size_Total (º )
J K H
Flash square
Flash bar
Latency (ms)
20
40
60
80
0 40 80
80
40
0
RF axis_Total (º )
180
90
0
0 90 180
RF axis_Total (º )
0 90 180
180
90
0
60
30
0
0 90 180
RF axis_Thal. (º )
0
90
180
Aspect ratio_Total
Aspect ratio_Thal.
1 2
1
2
Fraction (%)
0 2
0
10
20
0
2
0 2 0 30 60 1
1
LED on LED off
0
0
0 1
Figure 2.6 Intracortical excitation expands visual receptive field. A, Top left, stimulation of receptive field
(green) boundary correlated with the response delay. Bottom, superimposed average bar-evoked excitatory
currents without (red) and with (blue) LED illumination. Scale bars represent 0.1 nA (red) and 0.05 nA (blue),
and 0.5 s. Inside dodecagon, derived receptive fields before (red) and after (blue) cortical silencing. Scale bar
represents 10° . Top right, elliptical fitting of the receptive fields. B, Top, average excitatory currents to a flash
noise stimulus. Scale bars represent 50 pA and 50 ms. Bottom, onset latencies in LED off versus LED on trials
(64.1 ± 6.9 versus 64.8 ± 6.3 ms, P = 0.32, two-tailed paired t test, n = 19 cells from 19 mice; the same test applied
below). Error bars represent SD C, Spatial receptive field size derived for thalamocortical and total excitation
(mean ± SD marked). D, Angle of receptive field major axis (P = 0.52). E, Aspect ratio (1.63 ± 0.32 versus 1.68
± 0.29). Inset, distribution of aspect ratios of thalamocortical receptive fields. F, Derived major receptive field
axis versus measured preferred orientation (P = 0.54). G, OSI versus aspect ratio. Linear fitting: one-sided P =
3.3 × 10−4. H, Excitatory currents of an example cell to single flash squares at different locations without and
with LED stimulation. Scale bars represent 0.1 nA (left) and 0.052 nA (right), and 0.2 s. I–K, Spatial receptive
fields measured by flash stimuli (mean ± SD marked; n = 14 cells from 14 mice). J, P = 0.4. K, Aspect ratios =
1.60 ± 0.21 versus 1.58 ± 0.21, P = 0.46.
28

of LED illumination, while its overall shape was not changed significantly.
In a total of 19 recorded cells, we observed that the onset latency of excitatory responses to
moving bars (averaged for two opposite directions) increased more for the preferred than the
orthogonal orientation, suggesting more receptive field shrinkage along the preferred
orientation. As a control, the onset of responses to flash stimuli was not changed in the presence
of LED illumination (Figure 2.6B), indicating that the subcortical conduction of visual signals
was not affected by the cortical silencing. From the response onset latencies, the estimated
receptive field size (defined as the long axis of the fitted ellipse) for total excitatory input was
45.6  11.7 (mean  SD). The estimated receptive field was reduced to 32.4  10.2 after
cortical silencing (P  5.16e–10, two-tailed paired t-test, n  19 cells from 19 mice, Figure
2.6C). Despite the reduction in size, the receptive field shape remained roughly the same, as
reflected by the largely unchanged angle of the major receptive field axis (P  0.52, two-tailed
paired t-test, Figure 2.6D) and the unchanged aspect ratio (P  0.22, two-tailed paired t-test,
Figure 2.6E), which was defined as the ratio of the length of major versus minor receptive
field axis (Volgushev et al., 1996; Lampl et al., 2001). In addition, the major axis of the
estimated thalamocortical receptive field had a similar angle as the preferred orientation of the
isolated thalamocortical response (Figure 2.6F). All thalamocortical receptive fields were
slightly elongated, as reflected by the aspect ratios larger than 1 but mostly smaller than 2, with
a mean of 1.63 (Figure 2.6E). Furthermore, there was a strong linear correlation between the
orientation selectivity level of thalamocortical responses and the aspect ratio of the estimated
thalamocortical receptive field (Figure 2.6G).
29

To further confirm the receptive field shrinkage after cortical silencing, we applied
conventional flash sparse stimuli to directly map the spatial receptive field (see Methods). We
found that the receptive field indeed appeared smaller in the presence of LED illumination, as
shown by an example cell (Figure 2.6H). Summary results of 14 cells recorded from 14 mice
showed that receptive field size was significantly decreased by eliminating intracortical
excitatory inputs (from 38.2  9.0 to 31.8  8.6, P  1.92e–6, one-tailed paired t-test, Figure
2.6I), whereas the angle of receptive field major axis and the aspect ratio were unaltered (P
0.4, Figure 2.6J; P  0.46, Figure 2.6K; two-tailed paired t-test). Notably, in normal
conditions, the receptive field size measured with sparse flash stimuli was smaller than that
estimated from drifting-bar responses (P  0.02, one-tailed t-test), while they were not different
in cortical silencing conditions (P  0.87, two-tailed t-test). Therefore the receptive field size
derived from drifting-bar responses reduced more (29.4   9.8) after cortical silencing,
compared to that measured with flash stimuli (16.9   8.5, P  2.3e–4, one-tailed t-test).
One possible explanation was that some cortical neurons providing distant intracortical inputs
were sensitive to moving stimuli, but could not be activated by sparse flash stimuli. Altogether,
these results suggested that the spatial organization of thalamic inputs (i.e. the elongated
arrangement) provided a basis for the orientation tuning of thalamocortical responses, and that
intracortical excitatory circuits expanded the visual receptive field approximately
proportionally in spatial extent.
2.3.5 Tuning of dLGN neurons is unaffected
Previous studies indicate that layer 6 neurons in sensory cortices project back to the thalamus
and may modulate thalamic neuron activity (Cruikshank et al., 2010; Olsen et al., 2012). To
30

investigate the effect of silencing the cortical feedback projection on thalamic activity, we
carried out cell-attached recordings in the dLGN. We found that neurons in the dLGN already
exhibited moderate orientation tuning as measured by either drifting bars (Figure 2.7A-C) or
by drifting sinusoidal gratings, consistent with a recent report (Piscopo et al., 2013). Their
tuning was not significantly affected by cortical silencing (Figure 2.7A-C), the effectiveness
of which was verified in each experiment by recording in layer 6. The evoked firing rates of
–90 0 90
0
0.5
1.0
C
B
D
A

Cortical layer 6
0.5 s
5 Hz
dLGN
90º
270º
0º 180º
25 spikes
0.5 s
60 Hz
E
gOSI
L4 dLGN Thal.
***
*** *
gOSI_LED On
gOSI_LED off
0 0.1 0.2
0
0.1
0.2
θ(º )
Norm. FR
LED on
LED off
Spike number
LED on
Spike number
LED off
0
10
20
0
10 20
0
0.5
1.0
F
90º
270º
0º 180º
16 spikes
Cortical layer 6

0.5 s
4 Hz
TRN
0.4 s
30 Hz
gOSI_LED on
G
H
FR (Hz)
LED off LED on
0
10
20
gOSI_LED off
0 0.1 0.2
0
0.1
0.2
Figure 2.7 Orientation tuning of thalamic neurons. A, Top, PSTHs for visually evoked spikes in a layer 6
neuron. Middle, PSTHs for responses to drifting bars without (black) and with (blue) LED illumination of a dLGN
neuron in the same mouse. Bottom, polar plots of average spike count. B, OSI of dLGN neuron responses (LED
on, 0.093 ± 0.052; LED off, 0.089 ± 0.054; P = 0.48, two-tailed paired t test, n = 18 cells from 12 mice). C,
Average normalized tuning curves for dLGN neurons. Error bars represent SEM. D, Evoked spike numbers for
dLGN neurons (LED on, 10.4 ± 4.9; LED off, 10.9 ± 5.3; P = 0.27, two-tailed paired t test, n = 18 cells from 12
mice). E, Distribution of OSIs for dLGN neuron spikes, thalamocortical excitation and layer 4 neuron spikes to
drifting bars (n = 18, 19 and 33 cells from 12, 19 and 25 mice, respectively). ***P = 5.4 × 10−10 and 1.4 × 10−11
(top); *P = 0.022, one-way ANOVA post hoc test (Tamhane’s T2 test). Error bars represent SD. F, Spike
responses of an example TRN neuron to drifting gratings (three cycles). Data are presented as in A. G, Average
evoked firing rates of TRN neurons (LED on, 2.8 ± 2.3; LED off, 10.2 ± 4.6 Hz; P = 3.6 × 10−9, one-tailed paired
t test, n = 20 cells from 16 mice). H, OSI of TRN neuron responses (LED on, 0.040 ± 0.027; LED off, 0.044 ±
0.025, P = 0.59, two-tailed paired t test, n = 20 cells from 16 mice).
31

dLGN neurons averaged for twelve directions were unaltered after silencing the cortex (Figure
2.7D), indicating that the reduction of excitatory drive in cortical neurons could be attributed
primarily to the elimination of intracortical inputs. The tuning strength of dLGN neuron
responses was slightly stronger than that of thalamocortical input, but was much weaker than
that of cortical neuron spikes (Figure 2.7E).
In contrast to dLGN neurons, the evoked firing rate of TRN neurons was markedly reduced
after silencing the cortex (Figure 2.7F, G). These neurons essentially had no orientation tuning
(Figure 2.7H). Their average OSI was 0.044  0.025 (mean  SD, n  20 cells from 16 mice),
significantly lower than that of dLGN neurons (P  0.0018, one tailed t-test). That the firing
rate of dLGN neurons was unchanged after silencing the cortex was likely due to a concurrent
decrease of excitatory drive from layer 6 and inhibitory drive from the TRN (Cruikshank et
al., 2010; Olsen et al., 2012), which also receives direct excitation from layer 6 of the cortex
(Cruikshank et al., 2010; Olsen et al., 2012).
Discussion
As a fundamental computational property, orientation selectivity is thought to emerge in the
visual cortex. Whether its generation in the thalamorecipient neurons can be solely attributed
to the spatial arrangement of feedforward thalamic inputs or intracortical circuits (in particular
the local recurrent network) play an indispensable role has been widely discussed (Ferster and
Miller, 2000). In this study, by silencing intracortical excitatory connections with an
optogenetic method, we showed that the feedforward input to mouse layer 4 excitatory neurons
was weakly orientation-tuned. Intracortical excitation scaled up or linearly amplified the
32

thalamocortical signals approximately threefold without modifying the input tuning property.
Similarly, the direction tuning provided by thalamocortical input was unaffected through such
signal amplification. In addition, our study revealed that intracortical excitatory circuits
enlarged the visual receptive field without significantly modifying the receptive field shape.
The linear amplification of thalamocortical responses suggests that the feedforward input,
although only weakly tuned, provides an orientation bias for driving orientation selectivity in
the cortex. The tuning of thalamocortical input can be contributed by several mechanisms.
First, the thalamocortical receptive field was slightly elongated, and the axis of elongation was
the same as the preferred orientation of thalamocortical responses to drifting bars. These results
are in line with the original feedforward model that the spatial organization of thalamic inputs
provides a fundamental basis for orientation tuning (Hubel and Wiesel, 1962). Because the
elongated spatial arrangement of thalamic inputs, a bar of preferred orientation can activate
thalamic inputs more synchronously than a bar of orthogonal orientation. More synchronous
inputs can generate a larger peak current, and can be more efficient in driving spiking of layer
4 cells (Bruno and Sakmann, 2006). Second, dLGN neurons themselves were orientation
tuned. The convergence of dLGN inputs with similar orientation preference might be sufficient
for providing orientation-tuned input to a cortical neuron. However, without understanding the
relationship between orientation preferences of dLGN neurons and their cortical targets, the
contribution of tuning of individual dLGN neurons remains unclear. Furthermore, the
segregation of ON and OFF thalamic inputs (Ferster and Miller, 2000; Jin et al., 2011a), which
has not been examined in the current study, may also contribute to the orientation tuning of the
summed thalamocortical input.
33

Previous studies in cat visual cortex have been focused on membrane potential responses
(Ferster et al., 1996; Chung and Ferster, 1998), which reflect a result of interplay between
excitatory and inhibitory inputs. The combining of optogenetic silencing with voltage-clamp
recordings allows the direct elucidation of different excitatory components and determination
of their respective contribution to cortical functional properties. Similar as in the cat visual
cortex, we did not find evidence that intracortical excitatory circuits significantly sharpen
orientation tuning, which had been predicted by previous theoretical models based on recurrent
circuits (Ben-Yishai et al., 1995; Douglas et al., 1995; Somers et al., 1995). Instead, excitatory
responses were scaled up by a similar factor across different orientations. Such scaling or gain
modulation of feedforward thalamocortical signals determined that the total excitation
remained weakly tuned. The orientation selectivity of spike responses of cortical neurons was
much stronger than their thalamic inputs (Figure 2.7E). The sharp selectivity of output
response may be eventually achieved through the effects of more broadly tuned inhibition
(Kerlin et al., 2010; Ma et al., 2010; Ko et al., 2011; Liu et al., 2011; Atallah et al., 2012; Lee
et al., 2012; Li et al., 2012b; Wilson et al., 2012) as well as of spike threshold (Priebe and
Ferster, 2008; Liu et al., 2010; Katzner et al., 2011; Liu et al., 2011; Tan et al., 2011). In
addition, non-linear mechanisms not revealed by the voltage-clamp recordings, e.g. NMDA
receptor activation (Branco et al., 2010), may also serve to sharpen the tuning of output
response.
What kind of intracortical circuits might be responsible for the multiplication of
thalamocortical signals? Neurons with different orientation preference in the mouse visual
cortex are organized in a random, “salt-and-pepper” pattern (Ohki et al., 2005; Ko et al., 2011).
34

However, the connection probability between excitatory neurons with a similar preferred
orientation is slightly higher than that between neurons preferring different orientations (Ko et
al., 2011). Such biased connectivity between cortical excitatory neurons is likely sufficient for
generating the weakly tuned intracortical excitation, which is also co-tuned with the
feedforward excitation. The cortical gain is roughly 2, tripling the amplitude of feedforward
input. The gain modulation of excitatory drive by intracortical circuits ensures that feedforward
signals are reliably and faithfully represented in the cortex.
On the other hand, intracortical circuits may provide opportunities for integrating novel
information by expanding the visual receptive field. It has been thought that horizontal or
lateral interactions contribute to the “silent” extra-classical receptive field, activation of which
provides contextual information that can modulate responses to stimulation of the central
classical receptive field of the cell (Allman et al., 1985; Gilbert and Wiesel, 1990; Levitt and
Lund, 1997). Here we provided direct evidence that visual receptive field peripheries might be
attributed to intracortical circuits. Notably, the recruitment of more distant inputs through
intracortical circuits largely preserved the elongated shape of the receptive field, suggesting
that the spatial integration of intracortical inputs had a bias along the preferred orientation of
the cell. That is, there might be more inputs arranged along the preferred orientation than the
orthogonal, contributing to the tuning of intracortical excitation (Chisum et al., 2003). Such
connectivity pattern may arise during development under the guidance of correlation-based
Hebbian plasticity rules (Clopath et al., 2010; Ko et al., 2013). The coherent organization of
thalamocortical and intracortical inputs allows cortical neurons to faithfully reinforce the
representation of thalamocortical information.
35

Chapter 3: Synaptic Circuits Underlying Orientation
Selectivity at Different Contrasts in Layer 4
Introduction
Orientation selectivity in the visual cortex of several mammalian species has been found to be
contrast invariant, as demonstrated by a constant tuning bandwidth in the face of increasing
stimulus contrast (Movshon et al., 1978; Albrecht and Hamilton, 1982; Sclar and Freeman,
1982; Li and Creutzfeldt, 1984; Skottun et al., 1987; Alitto and Usrey, 2004; Niell and Stryker,
2008). Moreover, recent studies indicate that orientation selectivity sometimes even exhibits a
contrast-dependent sharpening (Alitto and Usrey, 2004; Johnson et al., 2008). These
experimental observations contradict the prediction of a broadening of orientation selectivity
at high contrast by a purely feedforward excitatory circuit, caused by an increased drive from
non-selective thalamocortical inputs (Hubel and Wiesel, 1962; Troyer et al., 1998; Ferster and
Miller, 2000). Two mechanisms have been proposed to explain the contrast-dependent
properties of orientation selectivity. First, cortical inhibition among neurons with different
orientation tuning or from untuned inhibitory neurons is employed in theoretical network
models to achieve contrast invariance (Somers et al., 1995; Troyer et al., 1998; Lauritzen and
Miller, 2003). Second, a trial-to-trial variability of visually-evoked membrane potential
responses and its contrast dependence is shown to be able to account for contrast invariance
(Anderson et al., 2000b; Finn et al., 2007; Sadagopan and Ferster, 2012). Although the
variability model is intended for explaining contrast invariance with pure excitatory
mechanisms, potential contributions of inhibition should not be omitted. This is because the
36

membrane potential response results from the integration of visually-evoked excitatory and
inhibitory conductances and several intracellular recording studies demonstrate that there can
be substantial temporal overlaps between them (Borg-Graham et al., 1998; Liu et al., 2010;
Tan et al., 2011; Li et al., 2012a). In addition, there has been ample evidence from both
extracellular and intracellular studies suggesting that cortical inhibition may play a role in
sharpening orientation selectivity (De Valois et al., 1982; Celebrini et al., 1993; Monier et al.,
2003; Ringach et al., 2003; Marino et al., 2005; Xing et al., 2011). To address whether and
how cortical inhibition contributes to maintaining sharp orientation selectivity in the face of
increasing contrast, it is essential to elucidate the inhibitory response tuning at different
contrasts, and the responses of inhibitory neurons themselves to different contrasts as a
function of orientation. Such studies are still lacking.
The mouse has now become an important model to understand the mechanisms for visual
processing, due to the availability of recording techniques to dissect synaptic inputs and of
genetic tools to target inhibitory neurons (Taniguchi et al., 2011). Mouse visual cortical
neurons can be as sharply tuned for orientation as cat cells (Niell and Stryker, 2008; Liu et al.,
2009; Tan et al., 2011). Our previous study of mouse simple cells at a relatively high contrast
indicates that their orientation selectivity is significantly sharpened by very broadly tuned
inhibition (Liu et al., 2011). Here we report that excitatory and PV+ inhibitory neurons undergo
contrast-dependent sharpening and broadening of orientation selectivity respectively, and that
a broadening of inhibitory synaptic tuning contributes critically to maintaining sharp
orientation selectivity at high contrast.

37

Methods
3.2.1 Animal preparation
All experimental procedures used in this study were approved by the Animal Care and Use
Committee of USC. Adult female wild-type C57BL/6 mice (2-3 months) were used for
voltage-clamp recordings and some loose-patch recordings. For visually guided recordings
from layer 4, the layer 4-specific Cre mouse line, Scnn1a-Tg3-Cre (The Jackson Laboratory),
was crossed with the Ai9 reporter line which contains the DIO-tdTomato at ROSA26 locus
(The Jackson Laboratory). For recordings from PV+ neurons, the mice with PV+ neurons
expressing tdTomato were obtained by crossing the PV-ires-Cre driver line (The Jackson
Laboratory) with the Ai9 reporter line. Animal surgery was carried out as described in 2.2.2.
3.2.2 In vivo electrophysiology
The blind recording was performed as described in 2.2.3. The evoked excitatory and inhibitory
currents were resolved by clamping the cell at -70 and 0 mV, respectively. The blindly recorded
neurons assigned to layer 4 in this study had a cortical depth ranging from 375-520 µ m, which
was predicted from the travel distance of the pipette in the tissue. The layer assignment was
reasonably precise for the reason as described as in 2.2.3. The identified excitatory neurons
had a trough-to-peak interval of 0.89 ± 0.09 ms (mean ± SD, see Figure 1A inset) in their spike
waveforms.
In vivo two-photon imaging guided recording was performed as described in 2.2.4. The depth
of the recorded PV+ neurons in this study ranged from 350-465 μm beneath the pia. The
recorded PV+ neurons all exhibited narrow spike waveforms, with an average through-to-peak
38

interval of 0.30 ± 0.04 ms (see Figure 3.2 inset).
3.2.3 Visual stimulation
The visual stimuli were set as described as in 2.2.5. To measure orientation tuning, drifting
sinusoidal gratings (2 Hz, 0.04 cycle/° ) of 12 directions (30° step) and different contrasts were
applied in a random sequence. A stationary grating of one orientation was first presented on
the full screen for 1.8 sec before it drifted for 1.5 sec (3 cycles). The grating stopped drifting
for 500ms before another grating pattern appeared. The twelve patterns were presented in a
pseudorandom sequence. We applied 10 sets of stimuli to each cell, with the sequence different
between sets. The maximum contrast for the drifting gratings achieved by the monitor was
about 95% (near 100%).
3.2.4 Data analysis
The data acquisition and analysis of spike responses is the same as described in 2.2.7. The
recorded synaptic responses were cycle-averaged, and then were smoothed by averaging
within a 40ms sliding window. The orientation tuning curve was obtained and the gOSI was
calculated as described in 2.2.7. For measuring the tuning width, the response levels for drifting
sinusoidal gratings of two directions at the same orientation were averaged to obtain the
orientation tuning curve between 0-180 degrees, which was then fit with a Gaussian function
R(𝜃 ) = A ∗ exp(−0.5 ∗ (θ − φ)
2
/𝜎 2
) + B,  is the preferred orientation. Tuning width was
the half-width at half-maximum of the fit above the baseline B.
In voltage-clamp recordings, excitatory and inhibitory synaptic conductances were derived
according to the following equation (Zhang, et al., 2003; Tan, et al., 2004; Liu et al., 2007; Wu
39

et al., 2008):
𝐼 (𝑡 ) = 𝐺 𝑒 (𝑡 ) ∗ (𝑉 (𝑡 ) − 𝐸 𝑒 ) + 𝐺 𝑖 (𝑡 ) ∗ (𝑉 (𝑡 ) − 𝐸 𝑖 ) + 𝐺 𝑟 ∗ (𝑉 (𝑡 ) − 𝐸 𝑟 )
𝐼 (𝑡 ) is the amplitude of current at a time point. 𝐺 𝑒 (𝑡 ) and 𝐺 𝑖 (𝑡 ) are the excitatory and
inhibitory synaptic conductance respectively; 𝐺 𝑟 is the resting leak conductance. 𝑉 (𝑡 ) is the
membrane voltage; 𝐸 𝑒 (0 mV) and 𝐸 𝑖 (-70 mV) are the reversal potentials; 𝐸 𝑟 is the resting
membrane potential. 𝑉 (𝑡 ) is corrected by 𝑉 (𝑡 ) = 𝑉 ℎ
− 𝑅 𝑠 ∗ 𝐼 (𝑡 ), where 𝑅 𝑠 is the effective
series resistance and 𝑉 ℎ
is the applied holding voltage.
The estimated membrane potential response based on the synaptic conductances was derived
using a single-compartment neuron model (Somers et al., 1995; Troyer et al., 1998; Liu et al.,
2010):
𝑉 𝑚 (𝑡 + 𝑑𝑡 ) = −
𝑑𝑡 𝐶 [𝐺 𝑒 (𝑡 ) ∗ (𝑉 (𝑡 ) − 𝐸 𝑒 ) + 𝐺 𝑖 (𝑡 ) ∗ (𝑉 (𝑡 ) − 𝐸 𝑖 ) + 𝐺 𝑟 ∗ (𝑉 (𝑡 ) − 𝐸 𝑟 )] + 𝑉 𝑚 (𝑡 )
where 𝑉 𝑚 (𝑡 ) is the membrane potential at time 𝑡 , 𝐶 is the whole-cell capacitance. 𝐶 was
measured during the experiment. 𝐺 𝑟 and 𝐸 𝑟 were calculated based on the baseline currents at
two potentials.
3.2.5 Modelling
The synaptic conductance evoked by a moving grating was simulated by fitting the average
synaptic response waveform with a skew-normal function, which yielded a better fit than
sinusoidal functions or alpha functions (data not shown):
40

𝑓 (𝑥 ) = 𝑎𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 ∗
2
𝜔 ∗ 𝜑 (
𝑥 − ξ
𝜔 ) ∗ Φ ( α (
𝑥 − ξ
𝜔 )) + 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒
𝜑 and Φ are the standard normal probability density function and the cumulative distribution
function respectively. ξ determines the location. The scale factor 𝜔 was set at 145 ms and the
shape factor α at 1.5 for both excitatory and inhibitory conductances. The synaptic
conductance was set as 0 if it was smaller than 10% of maximum, as to generate a relatively
fast rising phase of the conductance change. Because the phase difference between excitation
and inhibition was 21º ± 15º (mean ± SD, n = 14), inhibition had a 25ms delay relative to that
of excitation (corresponding to an 18º phase difference). Varying the relative delay of
inhibition from 5 ms to 50 ms did not qualitatively affect our conclusion (data not shown). To
simulate responses to different orientations, while maintaining the temporal shape of the
evoked conductance its peak amplitude was varied according to the simulated tuning curve
created based on the average synaptic tuning curve in our experimental data. The amplitudes
of optimally evoked excitatory and inhibitory conductances were set based on the average
experimental data (1.37 nS for excitation and 2.94 nS for inhibition at 25% contrast; 2.14 nS
and 4.43 nS at near 100% contrast). The same conductance-based neuron model described
earlier was used to simulate the membrane potential response generated by the integration of
model synaptic conductances. 𝐸 𝑟 was –65 mV. 𝐸 𝑒 and 𝐸 𝑖 were 0 mV and -75 mV respectively.
𝐶 was 20 pF. Gr was calculated based on the equation: 𝐺 𝑟 = 𝐶 ∗ 𝐺 𝑚 /𝐶 𝑚 , where 𝐺 𝑚 , the
specific membrane conductance, is 2e–5 S/cm
2
, and 𝐶 𝑚 , the specific membrane capacitance,
is 1e–6 F/cm
2
.

41

Results
3.3.1 Contrast-dependent sharpening of orientation selectivity of excitatory
neurons
To first understand contrast-dependent orientation tuning properties of mouse visual cortical
neurons, we carried out in vivo loose-patch recordings in V1. Since contrast invariance is a
prominent problem for layer 4 neurons receiving direct thalamic inputs (Ferster and Miller,
2000), we focused our study in layer 4. Taking advantage of a layer 4 specific Cre driver line,
Scnn1a-Tg3-Cre (Madisen et al., 2010), we performed visually guided recordings from layer
4 neurons labelled by expressing a red fluorescence protein, tdTomato, under a two-photon
microscope (see Methods). Spike responses of the patched neuron to drifting sinusoidal
gratings of different contrasts ranging from 10% to near 100% were recorded, as shown by an
example cell (Figure 3.1A). The broad spike waveforms (Figure 3.1A, inset) indicated that it
was most likely an excitatory neuron (Liu et al., 2009). With contrast increasing, the spike
response of the cell (as measured by the average spike rate within all stimulus cycles) to the
preferred orientation increased drastically, while that to the orthogonal orientation did not
(Figure 3.1A, 3.1B). The tuning curve of evoked spike rate appeared sharper at 25% than 10%
contrast, as evidenced by the narrower tuning width. Higher than 25% contrast, the tuning
width changed little. The plot of evoked spike rate at all testing contrasts shows that spike rate
at the preferred orientation increased with increasing contrast, while that at the orthogonal
orientation barely changed (Figure 3.1C). The sharpness of tuning, as quantified with gOSI,
was steadily increased from low to high contrast (Figure 3.1D). Figure 3.1E shows the polar
graph plots of responses of three example excitatory neurons recorded blindly in wild type
42

mice (see Methods). In general, they showed similar contrast-dependency as the cell in Figure
1A. The first two cells were sharply tuned. Their tuning sharpness was apparently stable
between 25% and near 100% contrast. The third cell was only moderately tuned and had a
higher response level compared to the first two. Nevertheless, its tuning shape was not
dramatically changed by increasing contrast.
Data on layer 4 excitatory neurons either visually recorded in Scnn1a-Tg3-Cre mice or blindly
recorded in wild type mice were summarized (Figure 3.1F-3.1G). There appeared to be two
phases for the change of gOSI: an initial phase in which gOSI increased quickly, and a later
phase (above 37.5% contrast) in which gOSI increased more slowly (Figure 3.1F, left). The
average tuning width was decreasing in the initial phase, whereas it was relatively stable in the
second phase (Figure 3.1G). Thus, overall there is a sharpening of orientation tuning with
increasing contrast, but within a range of mid to high contrast (37.5% to near 100%) the results
are consistent with the notion of contrast invariance (i.e. tuning width being constant). There
is a drastic difference in contrast-dependency between responses to the preferred and
orthogonal orientations: the spike rate at the preferred orientation was elevated with increasing
contrast with the half elevation point occurring at around 30% contrast, whereas that at the
orthogonal orientation was on average unchanged (Figure 3.1H). Based on these results on
spiking response, we chose to compare excitatory and inhibitory synaptic responses between
contrast levels of 25% and near 100% in the later experiments. As summarized in Figure 3.1I,
from 25% to near 100% contrast, the spike rate at the preferred orientation increased by 74%
± 18% (mean ± SD, p < 0.001, paired t-test), while that at the orthogonal orientation did not
change significantly (-7% ± 15%, p > 0.1, paired t-test). This led to a 63% ± 18% increase in
43

0.0 0.5 1.0
0.0
0.5
1.0
0 50 100
0
3
6
Pref
Orth

30 120 210
0
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***
***
*
0 50 100
0.0
0.5
1.0
Pref
Orth
Contrast(%)
Norm. FR
H
A
10% 25% 50% near 100%
Contrast(%)
gOSI
θ(º )
FR (Hz)
I
0 90 180
0
3
6
10%
25%
50%
100%
FR (Hz)
θ(º )
Contrast(%)
FR (Hz)
B
C
D
E
Pref Orth gOSI TW
1ms
% Change
Cell #4
Phase 1 Phase 2
0 50 100
1.0
1.5
*
*
G
Norm. Tuning width
Contrast(%)
*
layer 4
(6) (12) (14) (14)
270
o
0
o
90
o
180
o
(3) (6) (10) (10)
(2) (2) (4) (6)
Cell #2
Cell #3
10% 25% 50% near 100%
Norm. gOSI
F
Contrast(%)
Phase 1 Phase 2
0 50 100
0.0
0.5
1.0
Norm. gOSI
gOSI (100%)
gOSI (25%)
J
0.49 ±0.18 0.59 ±0.06 0.77 ±0.02 0.82 ±0.03
0.15 ±0.07 0.43 ±0.07 0.49 ±0.06 0.51 ±0.04
0.19 ±0.05 0.21 ±0.04 0.25 ±0.03 0.31 ±0.02
All cycles 1st cycle
Contrast(%)
Phase 1 Phase 2
44

the value of gOSI (p < 0.001, paired t-test). In the meantime, tuning width was slightly but
significantly reduced (p < 0.05, paired t-test). In the mouse visual cortex, a reduction in
response over time (i.e. adaptation) was often observed (Tan et al., 2011; Li et al., 2012a). To
avoid potential confounds of changes of orientation selectivity over time, we also analyzed the
spike rate within the first cycle. As shown in Figure 3.1F (right), the analysis of the first cycle
also revealed an increase in gOSI with increasing contrast. Furthermore, we performed
bootstrap resampling (1000 times) of evoked responses as to overcome some random variation
during one stimulus presentation (Efron and Tibshirani, 1993; Carandini et al., 1997). Figure
3.1J shows the mean of bootstrapped gOSIs at 25% and near 100% contrasts for individual
cells. 67% of the cells exhibited a significant increase in gOSI at the high contrast (bootstrap,
p < 0.01), while no cell exhibited a decrease in gOSI at the high contrast (Figure 3.1J).

Figure 3.1 Contrast-dependent changes of orientation selectivity of layer 4 excitatory neurons in mouse
visual cortex. A, Spike responses of an example layer 4 neuron to drifting sinusoidal gratings of different
contrasts (labeled on the top), recorded in a Scnn1a-Tg3-Cre tdTomato mouse. Each trace (1.5 s, 3 cycles)
represents the post-stimulus spike time histograms (PSTHs) from 10 trials for a particular stimulus. Calibration:
30 Hz, 100 ms. Blue arrows mark the preferred orientation. Bottom, Tuning curves of average spike rate within
the three cycles. Bar, SD. Gaussian fits of the tuning curves are shown. Boxed inset, Top left, PSTH for
spontaneous activity. Calibration: 3 Hz, 100 ms. Inset, Top right, Coronal section of the cortex showing specific
labeling of neurons in layer 4 (left; scale bar, 200 m), a two-photon image of the patched layer 4 cell (upper
right; scale bar, 20 m), and superimposed 50 individual spike waveforms of the cell (lower right). Note that the
interval between the trough and the peak is relatively long (0.5 ms). B, Superimposed fitted tuning curves at
different contrasts. C, The average spike rates at the preferred and orthogonal orientations plotted against contrast
for the same cell. D, gOSI plotted against contrast for the same cell. Black symbols, gOSI measured from average
observed values. Gray symbols, mean and SD of bootstrapped gOSIs (resampling, 1000 times). E, Polar graph of
evoked spike rate for another three example cells. The maximum axis value is labeled in the parentheses. The
mean  SD of bootstrapped gOSIs is shown for each cell. F, Average gOSI (normalized to the value at near 100%
contrast) plotted against contrast. Left, gOSI based on spike rate within all cycles. Right, gOSI based on spike
rate within the first cycle. Bar, SE; n=15 cells. G, Average tuning width (normalized to value at near 100%
contrast) plotted against contrast. *p<0.05, paired t test, compared with near 100% contrast. H, Average evoked
spike rate (normalized to the value of the preferred orientation at near 100% contrast) at the preferred and
orthogonal orientations. Dash line, Contrast at which the increase in spike rate at the preferred orientation has
reached half maximum. I, Percentage change in evoked spike rate (preferred and orthogonal orientation), gOSI,
and tuning width from 25% to near100%contrast. *p<0.05; ***p<0.001, paired t test. J, Plot of the mean of
bootstrapped gOSIs at near 100% contrast versus that at 25% contrast for individual cells. Solid black, Cells that
exhibit significant deviation from the identity line (bootstrap test, p<0.01).
45

3.3.2 Contrast-dependent excitatory and inhibitory synaptic responses
To dissect visually evoked excitatory and inhibitory synaptic inputs, we carried out whole-cell
voltage-clamp recordings using a cesium-based internal solution (see Methods). As shown by
two example cells (Figure 3.2A, 3.2C, 3.2E, 3.2G), we recorded excitatory and inhibitory
responses to gratings while clamping the cell’s membrane potential at two different levels, -70
mV and 0 mV respectively (Liu et al., 2010; Liu et al., 2011). To quantify the strength of
synaptic responses, we averaged response traces by trials and cycles, and then measured the
peak amplitude after smoothing the response trace with a 40ms sliding window for averaging.
The values of this peak amplitude were used to plot the synaptic tuning curves (Figure 3.2B,
3.2D, 3.2F, 3.2H). In both cells, excitatory and inhibitory responses at all orientations were
strengthened with increasing contrast, so that the offsets of the synaptic tuning curves were
elevated. While the excitatory response was similarly tuned (Figure 3.2F) or became slightly
more sharply tuned (Figure 3.2B) at the high contrast, the inhibitory tuning on the other hand
became apparently broadened (Figure 3.2D, 3.2H).
We recorded excitatory responses from a total of 26 neurons. In 14 of these cells recording was
maintained long enough so that inhibitory responses were also completely examined. The
summarized results show that from 25% to near 100% contrast excitation and inhibition were
increased markedly by more than 50% (Figure 3.3A). The relative increase of excitation was
similar between the preferred and orthogonal responses, suggesting a nearly multiplicative
change of excitatory input across orientations. The relative increase of inhibition at the
preferred orientation was similar to that of excitation, but the relative increase of inhibition at
the orthogonal orientation was much greater (Figure 3.3A). Due to these unbalanced increases
46

in excitation and inhibition, the E/I ratio at the orthogonal orientation was significantly
decreased (Figure 3.3B), in accompany with a reduction of inhibitory tuning selectivity
(Figure 3.3C). In the meanwhile, excitatory tuning selectivity tended to be slightly enhanced
(Figure 3.3C). A similar conclusion on a reduced inhibitory tuning selectivity could be made
when the peak amplitude during the first cycle was measured (Figure 3.3D, 3.3E, 3.3F), or
Excitatory (-70mv) Inhibitory (0mV)
25% 100%
25% 100%
A
E
25% 100%
25% 100%
C
G
-90 0 90
0
2
4

-90 0 90
0
2
4
-90 0 90
0.0
0.5
1.0
θ(º )
D
(nS)
(nS)
25%
near 100%
-90 0 90
0.0
0.5
1.0
-90 0 90
0.0
0.5
1.0
-90 0 90
0.0
0.5
1.0
θ(º )
B
(nS)
(nS)
25%
near 100%
-60 30 120
0
1
2
3
-60 30 120
0
1
2
3
-60 30 120
0.0
0.5
1.0
θ(º )
H
(nS)
(nS)
25%
near 100%
-60 30 120
0.0
0.5
1.0
-60 30 120
0.0
0.5
1.0
-60 30 120
0.0
0.5
1.0
(nS)
θ(º )
F
(nS)
25%
near 100%
near
near
near
near
Figure 3.2 Excitatory and inhibitory synaptic responses revealed by voltage-clamp recordings. A, Average
traces of excitatory responses of an example cell at25%and near100%contrast. Red dash lines, Pretrigger baseline.
B, Response tuning curve at 25% (top) and near 100% (middle) contrast for the same cell. Bottom, Normalized
response tuning curve at 25% (dash) and near 100% (solid) contrast. C, D, Inhibitory responses and tuning curves
for the same cell. E–H, Synaptic responses and tuning curves for another example cell. Data are presented in a
similar manner. Calibrations: A, 100 pA; C, 200 pA; E, 50 pA; G, 150 pA; 200 ms.
47

when the average conductance was measured (for inhibition, gOSI was 0.11 ± 0.02 at 25%
contrast, 0.05 ± 0.01 at near 100% contrast, mean ± SE, p < 0.01, paired t-test; for excitation,
gOSI was 0.06 ± 0.03 at 25% contrast, 0.08 ± 0.03 at near 100% contrast, p > 0.05). Nine out
of the 14 recorded cells exhibited a significant decrease in inhibitory tuning selectivity (Figure
3.3G). As a result, at near 100% contrast inhibitory tuning selectivity was significantly lower
than excitatory tuning selectivity in 9 out of 14 cells (Figure 3.3H, right). Despite the changes
in synaptic strength and selectivity, there was no significant change in preferred orientation for
either spike or excitatory response (Figure 3.3I, 3.3J), nor in similarity between preferred
orientations of excitation and inhibition (Figure 3.3K).
3.3.3 An inhibitory synaptic mechanism underlying contrast-dependent
sharpening of orientation selectivity
Both an increase in excitatory selectivity and a decrease in inhibitory selectivity can lead to an
elevation of gOSI of spiking response. Considering the magnitude of the respective change in
excitatory and inhibitory tuning (Figure 3.3C, 3.3F), it can be predicted that the reduced
inhibitory tuning selectivity would play a major role. To confirm this point, we simulated
contrast-dependent membrane potential responses with a conductance-based integrate-and-fire
neuron model, using parameter values (e.g. synaptic strength and synaptic tuning profile) as
observed in our experiments. We first averaged the normalized synaptic tuning curves of all
the cells. Again, the summary shows that the average excitatory tuning curve barely changed
at the high contrast, whereas the average inhibitory tuning curve significantly broadened
(Figure 3.4A). We simulated waveforms of visually evoked synaptic conductances (in one
cycle) with a skew normal function (Figure 3.4B, top), with the inhibitory response delayed
48

0.0 0.1 0.2
0.0
0.1
0.2
A
Pref Orth
% Change
0
100
200
Ex
In

**
***
0 90 180
0
90
180
φ (100%)
φ (25%)
I
Spike
D
% Change
Pref Orth
0
50
100
Ex
In

**
**
gOSI (100%)
gOSI (25%)
G
0.0 0.1 0.2
0.0
0.1
0.2
0.00
0.05
0.10
0.15
Ex
In
C B
gOSI
25% 100% 25% 100%
***
0.0
0.5
25%
100%

E/I ratio
Pref Orth
**
0 90 180
0
90
180
0 90 180
0
90
180
25%
100%
φ(Exc)
φ (Inh)
φ (100%)
φ (25%)
K J
Exc
F E
0.0
0.1
0.2
Ex
In
gOSI
25% 100% 25% 100%
***
0.0
0.5
25%
100%

E/I ratio
Pref Orth
**
0.0 0.1 0.2
0.0
0.1
0.2
gOSI (Exc)
gOSI (Inh)
gOSI (Inh)
gOSI (Exc)
H
25% near 100%
Cycle averaged
1
st
Cycle
Exc
Inh
Figure 3.3 Contrast-dependent changes of synaptic inputs to excitatory neurons. A, Percentage change in
response level at the preferred and orthogonal orientations, quantified by the peak amplitude of the cycle-averaged
response waveform. Bar, SE; n=14. **p<0.01, ***p<0.001, paired t test. B, E/I ratio for the two orientations at
different contrasts. **p<0.01, paired t-test. C, gOSIs of excitation (red, n=26) and inhibition (blue, n=14) at 25%
and near 100% contrast. Data points for the same cell are connected with a line. ***p<0.001, paired t test. D–F,
Summary results based on quantifications of the peak amplitude within the first stimulus cycle. Data are presented
in the same way as in A–C. G, Plot of the mean of bootstrapped gOSIs at near 100% contrast versus that at 25%
contrast for individual cells. For inhibition, dark blue indicates cells that exhibit significant deviation from the
identity line (p<0.01). H, Plot of the mean of bootstrapped gOSIs for inhibition versus that for excitation to the
same cell. Left, at 25% contrast. Right, at near 100% contrast. Solid black, Cells that exhibit significant deviation
from the identity line (p<0.01). I, Preferred orientation of spike response at near 100% contrast versus that at
25%. J, Preferred orientation of excitation at near 100% contrast versus that at 25%. K, Preferred orientation of
inhibition versus that of excitation.
49

relative to the excitatory response by 25 ms (see Methods). The membrane potential response
was then derived by feeding the simulated synaptic conductances into the neuron model
(Figure 3.4B, bottom). To simulate synaptic tuning curves for the model, the average recorded
synaptic tuning curves were fitted with Gaussian functions (Figure 3.4C). For simplicity, the
excitatory tuning curves at the low and high contrasts were chosen to be the same. As shown
in Figure 3.4D, the tuning curve of the derived postsynaptic membrane potential response
(PSP) was sharper when the synaptic amplitudes and tuning profiles at near 100% contrast
(red) were employed in the model, as compared to those at 25% contrast (solid black). The
sharper tuning was due to a much larger elevation of PSP response at the preferred than
orthogonal orientation (Figure 3.4D). This simulation result is largely consistent with our
experimental observation that with contrast increases the spiking response was markedly
increased at the preferred orientation but remained unchanged at the orthogonal (Figure 3.1H).
The sharpening of PSP response can be primarily attributed to the increased broadness of
inhibitory tuning, because strengthening the excitatory and inhibitory inputs normally while
artificially retaining the initial inhibitory tuning profile (as at 25% contrast) resulted in even
weaker tuning of PSP response (Figure 3.4D, dashed curve). This result in fact supports the
prediction from our previous modelling work that a balanced increase of excitation and
inhibition would result in a weakening of tuning selectivity of PSP response and that a
broadening of inhibition might be required to counteract this effect (Liu et al., 2011).
To further confirm that a sharpening of PSP response occurs due to the contrast-dependent
broadening of inhibition, we carried out current-clamp recordings from layer 4 excitatory
neurons, using a K
+
-gluconate based internal solution (see Methods). The subthreshold
50

0
10
20
30
-90 0 90
0.0
0.6
0.8
1.0
25%, Ge
25%, Gi
100%, Ge
100%, Gi
Norm. G
θ (º )
0
2
4
0.0 0.3 0.6
-60
-50
Ex
In
G(nS) Vm (mV)
t (s)
A
θ (º )
-90 0 90
0.0
0.6
0.8
1.0
25%
100%
-90 0 90
0.0
0.6
0.8
1.0
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100%
Norm. G
θ (º )
Exc Inh
C
B
D
Vm
25%
E F
270
o
0
o
90
o
180
o
(2) (5)
(40mv) (40mv)
near 100%
Vm
Spike
25% near 100%
gOSI
0.00
0.05
0.10
25% near100%
**
-90 0 90
0.0
0.8
1.0
25%
100%
100% no Inh broadening
Norm. PSP
θ (º )
-90 0 90
0
16
18
20
22
25%
100%
100% no Inh broadening
PSP (mV)
θ (º )
Pref Orth
Vm_Derived (mV)
**
*
**
**
0
10
20
30
25%
100%
100% no Inh broadening
G
Vm_Rec (mV)
**
*
Pref Orth
Figure 3.4 A broadening of inhibitory tuning primarily contributes to the contrast-dependent sharpening
of orientation selectivity. A, Average normalized excitatory and inhibitory tuning curves at the two different
contrasts (25% and near 100%). Bar, SE; n=14. B, Top, Simulated excitatory and inhibitory synaptic conductance
waveforms (1 cycle). Bottom, Membrane potential response generated by the integrate-and-fire neuron model
when the evoked synaptic conductances shown on top were integrated. C, Tuning profiles of excitation and
inhibition at 25 and 100% contrast applied in our model. Ge, excitation. Gi, inhibition. D, Tuning curves of PSP
generated by our model. Black, 25% contrast. Red, 100% contrast. Dash, 100% contrast but with the inhibitory
tuning profile prevented from broadening (i.e., it is the same as the one at 25% contrast). The applied strength of
synaptic conductance at different contrasts was according to the tuning curve shown in C. Right, Normalized PSP
tuning curves. E, Left, Average subthreshold Vm responses of an example cell recorded under the current-clamp
mode. Spikes were removed by a 15ms median filter before averaging. Each trace is 1.5 s. Right, Polar graphs of
evoke spike rate and peak depolarization voltage. F, gOSI of peak Vm response at 25% and near 100% contrast.
Data points for the same cell are connected with a line. **p<0.01, paired t-test; n=8. G, Left, Average peak Vm
responses at the preferred and orthogonal orientations from our current-clamp recording results. n=8. Right, Vm
responses derived by feeding the experimentally determined excitatory and inhibitory conductances into the
neuron model. White column represents the Vm response generated when the inhibitory tuning was prevented
from broadening (i.e., the recorded inhibitory responses at near 100% contrast are scaled according to the average
inhibitory tuning curve at 25% contrast). *p<0.05; **p<0.01, paired t=test. N=14.
51

membrane potential (Vm) response was obtained after removing spikes with a median filter
(Figure 3.4E, left). Although the Vm response was much more weakly tuned than the spiking
response (Figure 3.4E, right), we did observe that the gOSI of Vm response significantly
increased from 25% to near 100% contrast (Figure 3.4F). In the meantime, the elevation of
Vm response from 25% to near 100% contrast was larger at the preferred than the orthogonal
orientation (Figure 3.4G, left). Considering that the subthreshold membrane depolarization
can be clipped by the spike threshold, the elevation of Vm response at the preferred orientation
may even be underestimated. Thus, as expected from the modelling result, the Vm response
was indeed sharpened at the high contrast. Furthermore, using the same neuron model we
derived PSP responses from experimentally determined excitatory and inhibitory conductances
(Figure 3.4G, right). For the PSP derived in this manner, the response elevation from the low
to high contrast at the orthogonal orientation was indeed much smaller than that at the
preferred. However, it was markedly increased if we forced the inhibitory tuning not to become
broadened by scaling the recorded inhibitory responses at near 100% contrast according to the
average inhibitory tuning curve at 25% contrast (Figure 3.4G, right, white column). Together,
our simulation data strongly suggest that the contrast-dependent broadening of inhibition plays
a critical role in maintaining sharp orientation tuning at high contrast.
3.3.4 Contrast-dependent broadening of orientation selectivity of inhibitory
neurons
How does the inhibitory tuning become broadened at high contrast? One possibility is that the
spiking response tuning of individual inhibitory neurons becomes broadened. Parvalbumin -
positive fast-spiking neurons constitute the largest inhibitory neuron population in layer 4
52

0 50 100
0.0
0.1
0.2
0.3
0 50 100
0
20
40
Pref
Orth

0.0 0.2 0.4
0.0
0.2
0.4
0 90 180
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0 90 180
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15
Ex
PV+

0.0
0.2
0.4
0.6
*
Contrast(%)
FR (Hz)
A
10% 25% 50% near 100%
θ
FR (Hz)
0 90 180
0
20
40
10%
25%
50%
100%
FR (Hz)
0 50 100
0.0
0.5
1.0
Norm. gOSI
Contrast(%)
0 50 100
0.0
0.1
0.2
gOSI
F
0 50 100
0.0
0.5
1.0
Norm. gOSI
Contrast(%)
0 50 100
0.0
0.1
0.2
gOSI
B
C
D
1ms
E
PV #2
PV #3
10% 25% 50% near 100%
θ(º )
H
-100
0
100
200
300

% Change
Pref Orth gOSI
**
***
***
***
**
270
o
0
o
90
o
180
o
(4) (4) (6) (8)
(4) (6) (8) (16)
0 50 100
0.0
0.5
1.0
Pref
Orth
Norm. FR
Contrast(%)
0 50 100
0.0
0.5
1.0
Orth/Pref
G
gOSI (100%)
gOSI (25%)
I J
Evoked FR (Hz)
**
Spontaneous FR (Hz)
0.38 ±0.07 0.27 ±0.06 0.16 ±0.04 0.04 ±0.02
0.20 ±0.06 0.21 ±0.05 0.10 ±0.04 0.08 ±0.02
gOSI
Contrast(%)
All cycles 1st cycle
53

(Kawaguchi and Kubota, 1997; Gonchar et al., 2007; Xu et al., 2010), yet contrast-dependent
tuning properties of these neurons have not been characterized. We thus recorded from PV+
neurons by two-photon imaging guided loose-patch recordings in the PV-Cre tdTomato
transgenic mice (see Methods), and in two cases by identifying fast-spiking neurons in blind
recordings (Liu et al., 2009). We found that PV+ neurons exhibited very different behaviours
from excitatory neurons. As shown by an example cell in Figure 3.5A, the cell displayed
relatively well tuned spiking responses at 10% contrast. However, the response tuning became
more and more flattened with increasing contrast. At near 100% contrast, its tuning selectivity
was almost completely lost (Figure 3.5B). Different from excitatory neurons, the response
level at both the preferred and orthogonal orientations increased robustly (Figure 3.5C),
resulting in an almost zero gOSI at the highest contrast (Figure 3.5D). A weakening of tuning
selectivity was also evident from the polar graphs for another two example PV+ cells (Figure
3.5E). Summarized results for thirteen PV+ neurons show that their tuning selectivity was
monotonically decreasing with increasing contrast (Figure 3.5F). Their spike responses at the
preferred and orthogonal orientations were both elevated, but not in the same rate (Figure
Figure 3.5 Contrast-dependent broadening of spike response tuning of PV inhibitory neurons. A,
Poststimulus spike time histograms (PSTHs) for spike responses of an example PV neuron in layer 4. Calibrations:
30 Hz, 100 ms. Boxed inset, Top left, PSTH for spontaneous activity. Calibrations: 3 Hz, 150 ms. Inset, Top right,
Two-photon image of the PV+ neuron (red) patched by the glass electrode (green), and superimposed 50
individual spike waveforms of the cell. Note that the interval between the trough and the peak is much shorter
compared with excitatory neurons. Calibration: 15μm. B, Superimposed fitted tuning curves at different contrasts.
C, Evoked spike rates at the preferred and orthogonal orientations plotted against contrast for the same cell. D,
gOSI plotted against contrast for the same cell. Gray shows the mean ± SD of bootstrapped gOSIs (re-samplings,
1000 times). E, Polar graphs of evoked spike rate for another two PV neurons. F, Average gOSI (normalized to
the value at the lowest contrast) plotted against contrast. Inset, Average gOSI (absolute value) at different
contrasts. Bar, SE; n = 13. G, Average evoked spike rates (normalized to the value of preferred orientation at
near100%contrast) at the preferred and orthogonal orientations. Inset, Ratio of spike rate at the orthogonal versus
preferred orientation. H, Percentage change in evoked spike rate and gOSI value from 25% to near 100% contrast.
I, Plot of the mean of bootstrapped gOSIs at near 100% contrast versus that at 25% contrast for individual cells.
Solid black, Cells that exhibit significant deviation from the identity line (bootstrap test, p=0.01). J, Average
evoked (at near 100% contrast) and spontaneous spike rate for excitatory and PV+ neurons. *p=0.05, **p=0.01,
t-test.
54

3.5G). The relative increase of spike rate was much higher at the orthogonal orientation than
at the preferred, resulting in significantly reduced tuning selectivity at near 100% contrast
(Figure 3.5H). For individual cells, bootstrap test revealed that 10 out of 13 cells exhibited a
significant reduction in gOSI at the high contrast (Figure 3.5I). Finally, consistent with
previous reports (Ma et al., 2010; Atallah et al., 2012), PV+ neurons had significantly higher
spontaneous and evoked firing rates than excitatory neurons (Figure 3.5J). Together, our
Trial-to-trial variance
(mV
2
)
100%
0
20
40
60
25% 100%
0
20
40
60
25%
Pref Null B A
25% near 100%
D C
25% near 100%
Pref
Orth
Pref
Orth
0.000
0.005
0.010
0.015

Pref Orth
0.0000
0.0005
0.0010

Trial-to trial variance (nA
2)
Exc
Inh
25% 100% 25% 100%
Vm
Exc
Inh
Exc
Inh
Vm
** *
** ***
Figure 3.6 Trial-to-trial variability of synaptic responses. A, Average traces of membrane potential responses
of an example cell at the preferred (Pref) and orthogonal (Orth) orientations. Shading indicates SD, which was
calculated within a 40ms sliding window. Red dash line, Baseline. Calibrations: 10 mV, 200 ms. B, Trial-to-trial
variance of membrane potential responses at 25% and near 100% contrast for the preferred and orthogonal
orientations. N=8 cells. **p=0.01; ***p=0.001, paired t test. C, Average traces of excitatory (Exc) and inhibitory
(Inh) responses of an example cell at the preferred and orthogonal orientations and at both25%and near 100%
contrasts, with SD indicated by the shading. Calibrations: Exc, 30 pA; Inh, 82 pA, 200 ms. D, Summary of trial-
to-trial variance of synaptic responses. The maximum SD for each orientation was selected for this quantification.
N=14 cells. **p=0.05; **p=0.01, paired t-test.
55

results strongly suggest that PV+ inhibitory neurons are not contrast invariant. The broadening
of orientation tuning of these neurons likely contributes to the broadening of synaptic inhibition
to excitatory cells.
3.3.5 Trial-to-trial variability of synaptic responses
Previous studies in the cat have suggested that contrast-dependent changes in trial-to-trial
variability is sufficient for generating contrast invariance without reliance on inhibition (Finn
et al., 2007; Priebe and Ferster, 2008). Here we looked into this issue and found that the trial-
to-trial variability of membrane potential responses was indeed reduced at the high contrast for
both the preferred and orthogonal orientations (Figure 3.6A, 3.6B). At the synaptic level, we
found that the variability of excitatory responses significantly decreased at the high contrast
(Figure 3.6C, 3.6D), consistent with the notion of a reduced variability of thalamic inputs
(Sadagopan and Ferster, 2012). The variability of inhibitory responses also tended to reduce,
although the level of reduction did not reach statistical significance (Figure 3.6C, 3.6D). Thus,
the change in variability of excitatory responses directly contributes to that of membrane
potential responses. Besides broadened inhibition, the reduced response variability can also
contribute to preventing elevations of the null response, since it is equivalent to elevating the
spike threshold (Finn et al., 2007; Priebe and Ferster, 2008).

56

Discussion
3.4.1 Contrast invariance vs. contrast-dependent sharpening of orientation
selectivity
Contrast invariance is traditionally defined as a constant orientation tuning width in the face of
contrast increases. In the meanwhile, it has been reported for ferret and a subset of primate
visual cortical neurons that a global measure of orientation selectivity (e.g. circular variance)
exhibits contrast-dependent changes which indicate an increased selectivity level at high
contrast (Alitto and Usrey, 2004; Johnson et al., 2008). Our data on excitatory neuron tuning
measured with gOSI are consistent with the results in these specifies in that tuning selectivity
is enhanced with increasing contrast. The data on tuning width are also consistent with a
previous report in the mouse V1 showing that tuning width is not significantly changed from
mid to high contrast (50% — 100%) (Niell and Stryker, 2008). For a broader range of contrasts,
our data show that orientation tuning width is in fact reduced with increasing contrast. This
general trend of decreasing tuning width, however, does not contradict the notion of contrast
invariance at a mechanistic level. Tuning width being unchanged or reduced, the task the
cortical circuits need to achieve is the same: to keep selectivity sharp at high contrast. The
details of contrast-dependent changes of orientation tuning may depend on different species,
cortical layers or cell types studied. Our results that the spike rate of excitatory neurons at the
orthogonal orientation is not enhanced with increasing contrast highlights the essence of the
problem the visual cortex is commonly facing: how to prevent an elevation of the null response
while there is certainly a large increase of excitatory drive at the null orientation.
57

3.4.2 Inhibitory contribution to contrast-dependent sharpening of
orientation selectivity
In our previous study of orientation selectivity in layer 2/3 simple cells, we found that
inhibitory input is more broadly tuned than excitatory input ((Liu et al., 2011), also see (Atallah
et al., 2012)). Such broader inhibition can significantly sharpen the tuning of output response
compared to co-tuned inhibition (Liu et al., 2011). We also found in our initial modelling work
that simply scaling up excitation and inhibition in proportion would not lead to invariant tuning
but a broadening of output response (Liu et al., 2011). We have thus hypothesized that
differential contrast-dependent modulations of excitatory and inhibitory tuning might be
necessary for achieving contrast invariance.
In the current study, we have indeed observed that contrast changes modulate the two types of
synaptic input in different manners. Excitatory responses evoked by different orientations are
strengthened nearly multiplicatively, consistent with contrast-dependent increases in firing rate
of thalamic neurons (Finn et al., 2007; Olsen et al., 2012). Inhibitory responses are also
strengthened, but with a relatively greater level at the orthogonal orientation than at the
preferred, resulting in a broadening of the inhibitory tuning. This can be attributed to a contrast-
dependent broadening of spiking response tuning of individual inhibitory neurons, and
additionally a potential increase in convergence of inhibitory neuron inputs, which remains to
be investigated. Our modelling results indicate that the broadening of inhibition at high contrast
largely limits the elevation of membrane potential response at the null orientation despite the
greatly increased excitatory conductance. Otherwise, if inhibition is not broadened, the
elevation of the null Vm response would be similar to (or even greater than) that of the
58

preferred response, leading to a reduced selectivity of output response (Figure 3.4D, dash
curve; Figure 3.4G, white column). Thus, our data indicate that an inhibitory mechanism plays
a critical role in preventing a significant elevation of the null response at high contrast.
3.4.3 Trial-to-trial variability of synaptic responses
Previous studies in the cat have shown that the trial-to-trial variability of membrane potential
responses of simple cells is reduced with increasing contrast (Anderson et al., 2000b). It can
be attributed to a reduced variance of thalamic inputs, and is sufficient for accounting for the
contrast invariance of simple cells (Finn et al., 2007; Priebe and Ferster, 2008; Sadagopan and
Ferster, 2012). In the current study, we also observed a reduction of trial-to-trial variability of
membrane potential responses, which can be directly attributed to a decrease in variability of
excitatory responses. In addition, due to a saturating input-output transfer function as discussed
in our previous study (Liu et al., 2011), an increase in excitatory conductance with a fixed
variance would naturally result in a reduced variance of membrane potential response. Thus,
two factors (increased excitatory conductance and increased variance of excitatory
conductance) together can account for the reduced variability of membrane potential responses
at high contrast. The observed changes in membrane potential response variability, however,
cannot sufficiently explain the contrast-dependent changes in spike response tuning in the
current study. This is because in the absence of inhibitory broadening, there would be a large
increase in the null Vm response (Figure 3.4D, 3.4G). Reducing variability, which is
equivalent to elevating the effective spike threshold, would be incapable of suppressing the
null spike response while allowing a large increase in the preferred spike response, since it
affects both responses at the same time. After the broadened inhibition largely limits the
59

elevation of the null Vm response, the reduction in variability can further contribute to
suppressing the null spike response by elevating the spike threshold. This may explain why
there is an increase in the null Vm response at high contrast (Figure 3.4G) whereas the null
spike response is not changed (Figure 3.1H). It should be noted that the level of inhibitory
contribution to orientation selectivity may differ in different species.
3.4.4 Contrast-dependent broadening of orientation tuning of inhibitory
neurons
An interesting observation in this study is that orientation tuning of PV+ inhibitory neurons
becomes flattened as contrast increases. The largely untuned property of PV+ neurons at high
contrast is consistent with observations from several previous studies which examined PV+
neurons at a fixed contrast (Kerlin et al., 2010; Ma et al., 2010; Hofer et al., 2011; Kuhlman et
al., 2011; Zariwala et al., 2011; Atallah et al., 2012). In addition, the contrast-dependent
changes of PV+ neuron tuning may provide an explanation for the discrepancy of PV+ neuron
selectivity reported by different groups (Kerlin et al., 2010; Runyan et al., 2010), since different
absolute contrasts may have been applied. Interestingly, the behaviour of PV+ neurons in
response to contrast increases (i.e. concurrent increases in firing rate at all orientations) is
exactly what has been predicted for excitatory neurons by the original Hubel and Wiesel model
that has not taken into account of inhibition or variability (Ferster and Miller, 2000). In layer
4, PV+ neurons have been shown to receive thalamic input like excitatory neurons and to
provide them with fast feedforward inhibition (Gibson et al., 1999; Gabernet et al., 2005;
Cruikshank et al., 2007). Here, we propose that a simple thalamocortical circuit recruiting
feedforward inhibition can explain both the contrast-dependent sharpening of excitatory
60

neurons and the contrast-dependent broadening of PV+ inhibitory neurons (Figure 3.7). In this
circuit, PV+ neurons provide primary feedforward inhibition to the excitatory neuron of
interest. The PV+ neurons and the excitatory neuron receive similar thalamic inputs, as
evidenced by a similar preferred orientation between excitatory and inhibitory tuning across
contrasts (Figure 3.3K). With contrast increases, the thalamic inputs are strengthened,
increasing excitatory drive onto both the PV+ neurons and the excitatory cell. As PV+ neurons
receive relatively stronger thalamic input compared to excitatory cells (Gabernet et al., 2005;
Cruikshank et al., 2010; Schiff and Reyes, 2012), the increase in excitatory drive onto PV+
neurons can easily lead to an elevation of their firing rate at all orientations due to the “iceberg”
effect (Carandini and Ferster, 2000; Wehr and Zador, 2003; Zhang et al., 2003). The
strengthened and broadened inhibitory input from PV+ neurons can then counteract the
increased excitatory drive onto the excitatory cell, and maintain or even enhance the sharpness
of orientation tuning of its output response. Such synaptic circuitry mechanism may also
function in other sensory systems to achieve sharp tuning regardless of stimulus intensity.
Figure 3.7 A simple model for contrast-dependent sharpening of excitatory neurons and contrast-
dependent broadening of PV inhibitory neurons. The schematic orientation tuning curves of Vm response for
the PV neuron(s) and the excitatory (Ex) cell are shown. Gray, At a low contrast. Black, At a high contrast. Dotted
gray line, Spike threshold. The PV neuron(s) exhibits a similar preferred orientation as the excitatory cell, since
they receive a similar set of thalamic inputs.
61

Chapter 4: Synaptic Circuits of Direction Selectivity in
Simple Cell in Layer 4
Introduction
The analysis of object motion in the visual world is achieved through actions of direction-
selective neurons (Hubel and Wiesel, 1962). These neurons respond well to motion in one
direction across their visual receptive fields, but weakly or not at all to motion in the opposite
direction. Previous studies in the cat have suggested that direction selectivity arises anew in
the cortex (Wiesel and Hubel, 1966; Cleland and Levick, 1974; Dreher et al., 1976). Two
prominent models have been proposed for the generation of direction selectivity. In the first,
excitatory response latency shifts systematically across the receptive field, such that excitatory
inputs from different receptive field locations sum optimally for stimuli moving in the preferred
direction (Adelson and Bergen, 1985), resulting in direction tuning of excitatory input. This
pattern of temporal offsets underlies the well-documented “slant” in spatiotemporal response
maps of direction-selective cells (Movshon et al., 1978; Reid et al., 1987; McLean and Palmer,
1989; Albrecht and Geisler, 1991; Reid et al., 1991; DeAngelis et al., 1993; Emerson, 1997;
Livingstone, 1998). In the second, neither excitation nor inhibition is direction selective, and
direction selectivity arises from the interplay between excitation and inhibition. In this model,
inhibition is spatially asymmetric, i.e. it is preferentially localized to one side of the receptive
field (Barlow and Levick, 1965; Torre and Poggio, 1978; Hesam Shariati and Freeman, 2012).
A stimulus moving in the preferred direction evokes a large excitatory response before entering
62

the inhibitory region, whereas a stimulus in the null direction stimulates the inhibitory region
first, from which the inhibitory input effectively suppresses the later activated excitatory input,
due to its intrinsically longer delay compared to the excitatory input. More recently, by
applying a response latency difference (in milliseconds) to ON and OFF thalamic relay cells
as observed experimentally (Jin et al., 2011b), a theoretical study using feedforward networks
has successfully modeled cortical direction selectivity (Hesam Shariati and Freeman, 2012),
suggesting that different response latencies for light increments and decrements could also
potentially play a role in direction selectivity of simple cells which receive relatively balanced
ON and OFF thalamic inputs.
In the cat V1, an intracellular study examining excitatory and inhibitory synaptic inputs
underlying direction-selective responses of simple cells (Priebe and Ferster, 2005) has reported
that excitation and inhibition are similarly well tuned for the same direction, which can be
attributed to slanted spatiotemporal response maps. In addition, excitation and inhibition are
found to be organized in a “push-pull” pattern in the space-time domain, i.e. maximum
excitation occurs at the same time as minimum inhibition, and vice versa. Because of the
temporal separation between excitation and inhibition, inhibition is not thought to be able to
contribute to direction selectivity (Priebe and Ferster, 2005, 2008). Another study not
particularly focusing on simple cells generates a more diverse picture by showing that in a
significant portion of V1 cells maximum inhibition is evoked by null directional stimuli
(Monier et al., 2003). In the mouse visual cortex, analysis of the synaptic mechanisms
underlying direction selectivity has been essentially lacking, despite the emergent importance
of this model system for visual research. Recent in vivo Ca
2+
imaging and single-unit recording
63

studies in the dLGN have suggested that information carried by direction-selective retinal
ganglion cells may be channelled into the V1 through direction-selective dLGN neurons, which
comprise only a small fraction of the mouse dLGN population (Rochefort et al., 2011; Marshel
et al., 2012; Piscopo et al., 2013; Scholl et al., 2013; Zhao et al., 2013). Consistent with this
notion, it is found that thalamocortical input can be direction-tuned (Li et al., 2013; Lien and
Scanziani, 2013), suggesting that direction-tuned dLGN input may provide a scaffold for
cortical direction selectivity in the mouse V1. However, how cortical inhibitory inputs
contribute to direction selectivity is not known. Recent studies indicate that the “push-pull”
relationship between excitation and inhibition as observed in cat simple cells may be absent in
the mouse V1 (Liu et al., 2010; Liu et al., 2011; Tan et al., 2011), which suggests that inhibition
can intimately interact with excitation and influence the output response. In this study, we
applied in vivo whole-cell voltage-clamp recordings in layer 4 of mouse V1 to reveal excitation
and inhibition evoked by moving stimuli. We found in direction-selective cortical neurons that
excitation was tuned while inhibition was largely untuned. The tuning strength of excitation
positively correlated with the spatial skewness of the excitatory input receptive field, whereas
the inhibitory receptive field was more spatially symmetric. The differential spatial tuning
between excitation and inhibition was transformed into a temporal asymmetry of excitatory-
inhibitory interplay under movements of opposite directions, which facilitated an inhibitory
sharpening of direction selectivity in the mouse visual cortex.

64

Methods
4.2.1 Animal preparation
Animal was prepared as described in 3.2.1.
4.2.2 In vivo electrophysiology
The blind recording was performed as described in 2.2.3 and 3.2.2. In order to record from
fast-spiking inhibitory neurons, smaller pipettes (10 MΩ) were used and neurons with fast-
spike shapes were actively searched. One fast-spiking neuron could be encountered for every
5-10 attempts. All neurons recorded in this study were located at a depth of 375-520µ m below
the pia according to the microdrive reading, corresponding to layer 4 (Li et al., 2012b).
4.2.3 Visual stimulation
The general setup for visual stimulation is the same as described in 2.2.5. Drifting sinusoidal
gratings at 95% contrast were applied to evoke spike responses. Light or dark drifting bars
were applied from evoking membrane potential response and synaptic response. To map spatial
receptive field, bars (4   60) of optimal orientation and contrast at 15 positions were flashed
(duration = 80 or 120 ms, inter-stimulus-interval = 500 ms) in a pseudo-random sequence.
Each position was stimulated 10 times. Using 80 ms and 120 ms flashing bars generated similar
spatial tuning curves (data not shown).
4.2.4 Data analysis
The data acquisition and analysis of spike responses is the same as described in 2.2.7. Spike
shape was determined by averaging 50 individual spikes. Fast-spiking neurons were identified
65

by a narrow spike shape (tough-to-peak interval < 0.5 ms). Spikes evoked by drifting gratings
were counted within a window from 70 ms after the start of drifting to 70 ms after the end of
drifting. The mean spontaneous firing rate in the absence of visual stimulation was subtracted
from stimulus-evoked spike rates. Spike responses for each grating stimulus were cycled-
averaged across trials. The sinusoidal fitting of cycle-average responses at preferred direction
was used to calculate the mean (F0) and modulation amplitude (F1). Those neurons with
modulation ratios (F1/F0 ratios) larger than 1 are considered simple cells. In current-clamp
recordings with the K
+
gluconate-based intrapipette solution, spikes were removed with an 8
ms median filter (Li et al., 2012b) and the residual subthreshold Vm response was analyzed. In
voltage-clamp recordings, the excitatory and inhibitory responses traces were first smoothed
by averaging within a sliding 40 ms window (Li et al., 2012b), and the peak response relative
to baseline was then determined and used to plot tuning curves. The peak intracellular or spike
responses across directions were fit with two Gaussian curves centered on φpref and φpref + 180,
of equal variances (σ
2
) but different amplitudes (A1 and A2):

Direction preference was calculated based on the vector sum of the peak responses across
directions. ANOVA test was performed to determine if at least response at one direction was
significantly above others. Gaussian fitting was performed for cells that passed this test. From
this fit, we calculated a direction selectivity index as DSI = (Rpref – Rnull) / (Rpref + Rnull), where
Rpref = A1 + B and Rnull = A2 + B. The receptive field envelope of peak excitatory and inhibitory
response amplitudes was fitted with a skew-normal distribution function:

baseline
x x
amplitude x f 

 

  )) ( ( ) (
2
) (







B A A R
pref pref
            ) / ) ( 5 . 0 exp( ) / ) ( 5 . 0 exp( ) (
2 2
2
2 2
1
       
66

ϕ and Φ are the standard normal probability density function and its cumulative distribution
function respectively. ξ determines the location, ω is the scale factor, and α is the shape factor.
The skewness is given by:

The temporal overlap between excitatory and inhibitory response traces, and the spatial overlap
between excitatory and inhibitory receptive fields was quantified with an overlap index (OI),
which is given by: OI = (w1+w2-d)/(w1+w2+d). The w1 and w2 are the half-widths at half-
maximum of excitatory and inhibitory response waveforms or tuning curves respectively. The
d is the distance between the peaks of the two response waveforms or tuning curves under
comparison. OI = 1 gives a complete overlap, and OI ≤ 0 gives a complete separation.
Excitatory and inhibitory conduces were derived as described in 3.2.4.
4.2.5 Neuron model
We derived the membrane potential response in the absence of a spiking mechanism by feeding
the experimentally determined excitatory and inhibitory conductances into an integration-fire
neuron model as described in 3.2.4.
4.2.6 Simulation
We simulated the moving-bar response as the summation of responses to 15 sequential bars
(4º width) evenly spaced in time, corresponding to a moving speed of 50º /s. All the individual
bar responses had the same temporal profile. They were modeled as: 𝐺 = 𝐺 𝑚𝑎𝑥
∗
(1 − exp (−(𝑡 − 𝑡 0
)/𝜏 1
)) ∗ exp (−(𝑡 − 𝑡 0
)/𝜏 2
), for t > t0 . The t0 is the response onset latency.
2
2 / 3 2
3
1
,
) / 2 1 (
) / 2 (
2
4



 
  




 skewness
67

Fitting of the average response to flash bars yielded τ1 = 2.8 s and τ2 = 0.04 s. For each bar,
Gmax was determined by its position within the receptive field. The receptive field spatial tuning
curve was modeled as a skew-normal distribution function. The latency t0 was negatively
linearly correlated with the response amplitude, with the shortest latency = 50 ms and longest
latency = 100 ms.
Results
4.3.1 Direction selectivity of layer 4 neurons in mouse V1
With in vivo cell-attached loose-patch clamp recordings (Wu et al., 2008; Zhou et al., 2010),
we first examined direction selectivity properties of layer 4 neurons in the mouse V1. These
neurons have been shown to receive direct thalamocortical input (Li et al., 2013; Lien and
Scanziani, 2013). The recorded cells were categorized into putative excitatory and fast-spiking
inhibitory neurons, according to spike width (Figure 4.1A). Direction tuning was assayed by
applying single drifting bars or drifting gratings at twelve evenly spaced directions (see
Methods). We quantified the strength of tuning with a direction selectivity index (DSI). DSI
> 0.3 was used as a criterion for defining direction-selective neurons (0.2 used in (Conway and
Livingstone, 2003)). In our recorded excitatory neurons, about half (28 out of 52) were
direction-selective (Figure 4.1B). In contrast, the fast-spiking neurons, which were putative
parvalbumin-positive inhibitory neurons (Liu et al., 2009; Ma et al., 2010), exhibited much
weaker direction selectivity (Figure 4.1B). They were essentially untuned for direction (DSI
< 0.3), consistent with previous reports (Niell and Stryker, 2008; Ma et al., 2010).

68

Layer 4 excitatory neurons exhibited linear responses to drifting sinusoidal gratings, as
demonstrated by F1/F0 ratios larger than 1 (Figure 4.1C), which are a prominent feature of
-1.0 -0.5 0.0 0.5 1.0
0
5
10
15
Monocontrast index
Cell count
Layer 4
On Off
0 180 360
0
180
360
Pref. φ_Light bar (º )
Pref. φ_Dark bar (º )
C A
D
F
0
10
20
0
10
20
0 1 2
0
10
20
0
20
40
0
20
40
0 1 2
0
20
40
Light bar
Dark bar
Time (s)
Spike number
Cell #1 Cell #2
Time (s)
Pref
E
0.0
0.5
1.0

FS EX
DSI
1 ms
0.0 0.5 1.0
0
10
20
Cell number
Spike width (ms)
-1.0 -0.5 0.0 0.5 1.0
0
5
10
Layer 2/3
Monocontrast index
B
5Hz
EX
FS
0
0 1 2
0
5
10
F1/F0
Cell number
0 500
0
60
Time (ms)
Spike #
Figure 4.1 Direction selectivity of layer 4 neurons in mouse V1. A, Distribution of spike widths for recorded
layer 4 neurons. Spike width was quantified as the interval between the trough and peak of the average spike
waveform of the cell. Fast-spiking (FS) cells had spike widths < 0.5 ms. EX, putative excitatory cells.
Superimposed 50 individual spikes are shown for an example FS and EX cell respectively (top inset). B,
Distribution of DSIs for recorded fast-spiking (n = 23) and excitatory (n = 52) neurons in layer 4. Solid symbols
represent mean  SD. C, Distribution of F1/F0 ratios for layer 4 excitatory neurons. Top inset, cycle-averaged
post-stimulus spike time histogram (PSTH) for spike responses (baseline subtracted) of an example cell to
drifting sinusoidal gratings at preferred direction, fit to a sinusoid (dash red line). Amplitudes of F0 and F1 are
marked by red vertical bars. D, Distribution of monocontrast indices for excitatory neurons in layer 4 (left) and
layer 2/3 (right). The index was calculated as the difference between peak response levels in subfields of
preferred and non-preferred contrast (preferred – non-preferred), divided by their sum. The index is 1 if the cell
only shows responses to one contrast (ON or OFF). Top inset, spatial maps of ON and OFF spike responses of
an example layer 4 cells, which showed only an ON subfield (S1 cell). Scale bar: 10º . E, The preferred directional
angle φ measured with dark drifting bars versus that measured with light drifting bars. Red dash line is the
identity line. Each data point represents one cell. F, PSTH for spike responses of two representative cells to
bright and dark drifting bars of preferred orientation and different bar widths (4º , 8º , 16º , illustrated on the left).
Cell#1 had a dominant “OFF” subfield. Blue arrows mark the discharge response to the withdrawal of the light
bar from the OFF subfield, the timing of which changed with increasing bar width. Cell#2 had a dominant “ON”
subfield.
69

simple cells (Skottun et al., 1991). Notably, the receptive fields of these neurons were largely
dominated by responses to one sign of stimulus contrasts (ON or OFF), as demonstrated by
monocontrast indices close to 1 or -1 (Figure 4.1D). In contrast, neurons in superficial layers
(layer 2/3) exhibited more or less similar responsiveness to light increments (ON) and
decrements (OFF) (Figure 4.1D). Considering these response properties, the layer 4 excitatory
neurons mostly resembled previously described “S1” neurons in the cat/primate (Schiller et
al., 1976; Conway and Livingstone, 2003), i.e. simple cells with only one receptive field
subregion. Possibly due to this monocontrast property, the direction-selective layer 4 neurons
exhibited the same preferred direction when tested with light and dark bars (Figure 4.1E). This
could be explained by the observation that the response to a bar of non-optimal contrast in fact
reflected the “OFF” discharge when the bar left the subfield of the dominating contrast, as
demonstrated by the increasing delay of the “OFF” discharge with increasing bar widths
(Figure 4.1F). Since light and dark bars generated responses showing the same preferred
direction, in the next experiments, we applied drifting bars of the contrast the cell was most
sensitive to.
4.3.2 Direction tuning of subthreshold response
By applying whole-cell current-clamp recording with a K
+
gluconate-based internal solution
(see Methods), we compared direction selectivity of spike and membrane potential (Vm)
responses of the same neuron. Figure 4.2A shows the peri-stimulus spike time histograms
(PSTHs) for spike responses of a layer 4 excitatory neuron to drifting bars at 12 different
directions, as well as its subthreshold Vm responses after filtering out spikes (see Methods).
The cell responded maximally to a vertical bar drifting to the right, while having little response
70

to a bar drifting to the left, indicating that it was strongly direction selective. Different from
the spike responses, robust membrane depolarization responses were observed at all directions.
The polar graph plots of spike count and peak depolarization voltage demonstrate that the
strongest spike and Vm responses occurred at the same direction (Figure 4.2A, bottom panel).
A
C
0.00 0.05 0.10 0.15
0.0
0.5
1.0
r = 0.87
DSI_Spike
DSI_Vm
B
Vm
0 180
0
20
40
Vm (mV)
φ (º )
0 180
0
5
10
Spike #
φ (º )
270 °
90 °
180 ° 0 °

270 °
90 °
180 ° 0 °
Spike
0 180 360
0
180
360
Pref. φ_Spike (º )
Pref. φ_Vm (º )
Figure 4.2 Direction tuning of subthreshold membrane potential (Vm) response. A, Current-clamp
recording from an example cell. Left, PSTHs for spike responses to drifting bars at 12 directions. Right, average
Vm responses (10 trials) after filtering out spikes. Scale: 40 Hz / 18 mV, 100 ms. The stimulus direction is
indicated on the left. Solid arrowhead marks the preferred direction and open arrowhead marks the null direction.
Bottom panel, polar graph (left) and tuning curve (right) of spike count (red) and peak Vm (blue) responses.
Grey arrow indicates the preferred direction. Gaussian fits are shown. B, DSI of peak spike rate versus that of
peak Vm response. Red dash line is the identity line. Blue line is the best-fit linear regression line. The correlation
coefficient r is indicated. C, The preferred direction of spike response versus that of Vm response for cells with
DSI > 0.2. Red dash line is the identity line.
71

The spike response exhibited much stronger selectivity than the Vm response, as reflected by
the relative difference between the response magnitudes to the preferred and opposite (null)
directions.
We recorded from a total of 25 layer 4 excitatory neurons, and calculated DSIs after fitting
their response tuning curves with a wrapped Gaussian function (see Methods). As shown in
Figure 4.2B, DSI of spike response positively correlated with that of Vm response, but its
value was much higher than that of the Vm response. Notably, direction selectivity had been
amplified about six fold when Vm response was transformed into spike response. This result
is in agreement with the notion that spike thresholding can be a powerful mechanism for
sharpening feature selectivity of neuronal responses (Priebe and Ferster, 2008). In all the
recorded neurons, strongest spike and Vm responses were observed at identical or nearly
identical directions (Figure 4.2C). According to these results, direction-selective neurons can
be predicted base on the DSI of their Vm responses. In this study, we set this value at > 0.05
(corresponding to spike DSI > 0.3) as a criterion for identifying putative direction-selective
neurons.
4.3.3 Direction tuning of excitatory and inhibitory synaptic inputs
To address excitatory and inhibitory interactions underlying direction selectivity, we applied
in vivo whole-cell voltage-clamp recording with a Cs
+
-based internal solution to isolate
excitatory and inhibitory synaptic currents evoked by drifting bars (see Methods). Neurons
were first recorded under current-clamp mode to determine tuning of their Vm responses.
Excitatory currents were then recorded by clamping the cell’s membrane voltage at -70 mV,
and inhibitory currents were recorded at 0 mV (Liu et al., 2010; Liu et al., 2011). As shown by
72

an example cell in Figure 4.3A, we first determined that the cell was likely a direction-
selective cell, as its Vm response had a DSI of 0.14. Its excitatory responses displayed a clear
direction bias, which was the same as that of the Vm response. A direction bias of its inhibitory
responses however was not obvious, indicating that the inhibition was nearly untuned.
In a total of 19 putative direction-selective excitatory cells, we observed that the preferred
direction of Vm response was essentially identical to that of excitatory input (Figure 4.3B).
More importantly, the DSI values for Vm response strongly correlated with that for excitatory
A
0 180 360
0
180
360
Pref. φ_Vm (º )
Pref. φ_Exc (º)
0.0 0.1 0.2
0.0
0.1
0.2
r = 0.76
k = 1.05
DSI_Vm
DSI _Exc
0.0 0.1 0.2
0.0
0.1
0.2
DSI _Exc
DSI_Inh
0.0
0.1
***
0 180 360
0
180
360
Pref. φ_Exc (º)
Pref. φ_Inh (º)
B C D E
Vm
Inh
0 180
0
10
20
0 180
0
1
2
Exc
0 180
0
2
4
Vm
Inh
Exc
(mV)
(nS)
(nS)
φ(º )
270 °
90 °
180 ° 0 °
270 °
90 °
180 ° 0 °
270 °
90 °
180 ° 0 °
Figure 4.3 Direction tuning of excitatory and inhibitory synaptic inputs. A, Left panel, sequentially recorded
Vm, excitatory (Exc) and inhibitory (Inh) responses to drifting bars in an example cell. Scale, 10 mV/ 68 pA /121
pA, 100 ms. Right panel, corresponding polar graphs and tuning curves. Grey arrow indicates the preferred
direction. B, The preferred directional angle of Vm response versus that of excitatory input (n = 20). Red dash
line is the identity line. C, DSI of Vm response versus that of excitatory input. Blue dash line is the best-fit linear
regression line. The correlation coefficient r and slope k are indicated. D, DSI of inhibition versus that of
excitation. Red dash line is the identity line. In this analysis, only cells with DSI_Vm > 0.05 were included. Inset,
average DSI of excitation (red) and inhibition (blue). ***, p<0.001, paired t-test, n = 12. E, The preferred
directional angle of inhibition versus that of excitation. Red dash line is the identity line.
73

inputs (Figure 4.3C). These results support the notion that direction selectivity of layer 4
excitatory neurons originates from direction-tuned excitatory input. That is, the initial bias of
excitatory input sets the basis for direction selectivity. In a total of 12 cells with Vm DSI >
0.05 and with both excitatory and inhibitory responses recorded, we found that DSI of
inhibition was always lower than that of excitation (Figure 4.3D). The average DSI for
inhibition was 0.04 ± 0.01 (mean ± SD, n =12), while that for excitation was 0.12 ± 0.02 (p <
0.001, paired t-test). Nevertheless, the preferred direction of inhibition was essentially the same
as that of excitation (Figure 4.3E). Therefore, the strongest inhibition was evoked by a bar that
also evoked the strongest excitation, but the direction tuning of inhibition is much weaker than
that of excitation. The largely untuned property of inhibition was consistent with the
observation that fast-spiking inhibitory neurons were mostly untuned (Figure 4.1B).
4.3.4 Inhibition sharpens direction selectivity
Due to the differential tuning of excitation and inhibition, i.e. inhibition being much less
selective than excitation, the excitation/inhibition (E/I) ratio was significantly lower at the null
than the preferred direction (Figure 4.4A). This differential E/I balance for preferred and null
directional stimuli may potentially lead to a sharpening of direction selectivity of output
responses. To test this possibility, we compared the temporal profiles of excitatory and
inhibitory responses to the same drifting bar. Different from previous results in the cat V1
(Priebe and Ferster, 2005), we found that for both preferred and null directions, there was a
large temporal overlap between excitation and inhibition (Figure 4.4B). The average temporal
overlap index (OI) between excitatory and inhibitory response traces (see Methods) was 0.78
± 0.07 (mean ± SD) for the preferred direction, and 0.83 ± 0.10 for the null direction. This
74

indicates that inhibition can closely interact with excitation and suppress the membrane
depolarization response at both the preferred and null directions, and that inhibition may exert
a sharpening effect by suppressing the null response more effectively than the preferred
response.
To directly test the inhibitory influence on direction selectivity, we applied a conductance-
based integrate-and-fire neuron model (Liu et al., 2010; Liu et al., 2011; Li et al., 2012b) to
derive the expected membrane potential (Vm) responses in the presence and absence of
inhibition. The experimentally determined excitatory and inhibitory conductances evoked by
drifting bars were applied in the neuron model (see Methods). As shown in Figure 4.4C, when
excitatory inputs (Exc) alone were transformed into Vm responses (Vm_D(E)), there was a
significant reduction in the strength of selectivity. This observation is consistent with previous
reports that the non-linear membrane filtering attenuates response selectivity (Liu et al., 2011).
To be more specific, because the relationship between the Vm response amplitude and
excitatory conductance (i.e. input-output function) is a concave and saturating function (Liu et
0.0
0.5
1.0
Temporal OI
Pref Null
Exc
Inh
0.0
0.5
1.0
E/I ratio
Pref
***
Null
C A B
0.0
0.1
0.2
0.3
DSI
Exc Vm_D(E) Vm_D(E+I) Vm_R
***
***
***
Figure 4.4 Inhibition sharpens direction selectivity of membrane potential response. A, E/I ratios for
responses at preferred and null directions. Data points for the same cell are connected with a line. ***, p = 1.7e-
4, one-tailed paired t-test, n = 12. Solid symbols represent mean ± SD. B, Temporal overlap indices for excitatory
and inhibitory responses at preferred and null directions (p = 0.12, two-tailed paired t-test, n =12). Inset,
superimposed normalized excitatory and inhibitory conductances evoked by the same moving bar in an example
cell. Scale: 0.5 s. C, DSI of excitatory input (Exc), derived Vm response when only excitation is present
(Vm_D(E)), derived Vm response when both excitation and inhibition are present (Vm_D(E+I)), and recorded
membrane potential response (Vm_R). ***, p < 0.001, one-way ANOVA and post hoc test, n = 12. Data from the
same cell were connected.
75

al., 2011), the Vm response amplitude relative to the excitatory conductance is smaller at larger
excitatory conductances. This could be partly due to the fact that the driving force for excitatory
currents is reduced as larger excitatory postsynaptic potentials (EPSPs) are generated (Liu et
al., 2011). As excitatory inputs were only moderately tuned, the attenuation of selectivity
would result in largely untuned Vm responses. Noticeably, when inhibition was incorporated,
the selectivity of Vm response (Vm_D (E+I)) was markedly enhanced (Figure 4.4C). More
importantly, DSI of derived Vm responses became similar to that of experimentally observed
Vm responses (Vm_R). These modeling results demonstrate that inhibitory inputs indeed have
sharpened direction selectivity of output responses.
4.3.5 Temporal offset between excitation and inhibition
Are there any other factors contributing to the inhibitory effect on direction selectivity besides
the different E/I ratio for opposite moving directions? When comparing the excitatory and
inhibitory response traces evoked by the same drifting bar stimulus, we found that although
excitation and inhibition were largely overlapping in the temporal domain, their peak response
time was different (Figure 4.5A). Excitation peaked earlier than inhibition at the preferred
direction, whereas this temporal sequence was reversed at the null direction (Figure 4.5A,
4.5C). In addition, the excitatory responses themselves exhibited a temporal asymmetry: the
interval between the response onset and response peak was shorter at the preferred than the
null direction (Figure 4.5D). On the other hand, such temporal asymmetry was not observed
for inhibitory responses: the peak time relative to the onset was about the same for the preferred
and null directions (Figure 4.5E). Since excitation rises to the maximum faster than inhibition
for movements in the preferred direction but slower than inhibition for movements in the null
76

0.0 0.5 1.0
0.0
0.1
0.2
r = 0.9
DSI_Exc
RF skewness_Exc
C
0.0 0.5 1.0
0.0
0.5
1.0
RF skewness_Exc
RF skewness_Inh
0.0
0.3
0.6

***
D
H
E
Null
0.0
0.3
0.6
Time to Peak (s)
Pref Null
0.0
0.5
1.0
Time to Peak (s)
Pref
***
F
0.0
0.5
1.0

Spatial OI
I
20
30
40
Exc Inh
RF peak position (º )
***
J
0.0 0.5 1.0
0.0
0.5
1.0
Pref
Null
Time to peak_Rec (s)
Time to peak_Est (s)
0.0 0.3 0.6
0.0
0.3
0.6
Pref
Null
Time to peak_Rec (s)
Time to peak_Est (s)
0.0
0.5
1.0
1.5
Exc
Inh

Pref Null
Latency of Peak (s)
*** ***
K
L
M
A
B
Pref Null
Exc
Inh
Pref Null
0.0 0.5 1.0
0
50
100
150
200
Latency (ms)
Norm. amplitude
0 5 10 15
50
100
150
Bar position
Latency
(ms)
0.0 0.5 1.0
0
50
100
150
200
0 5 10 15
50
100
150
Norm. amplitude
Latency (ms)
Bar position
Latency
(ms)
0.87
0.2
0.52
0
G
Exc Inh
Exc
Inh
Exc
Inh
Exc
Inh
77

direction, inhibition could be more effective in suppressing the excitatory response to the null
direction, thus contributing to the sharpening of direction selectivity of output responses.
4.3.6 Spatially asymmetric excitatory and symmetric inhibitory receptive
fields
The above temporal properties of synaptic responses suggest that the spatial organization of
excitatory input receptive field may be asymmetric so that it takes a bar a shorter time to arrive
at the receptive field peak when it comes from the preferred side than from the null side. To
test this possibility, we mapped the spatial receptive fields of synaptic responses with flashing
bars of optimal orientation and contrast at different spatial locations (see Methods). First of
all, we found that consistent with the temporal overlap between excitation and inhibition
evoked by moving stimuli, the excitatory and inhibitory receptive fields almost completely
overlapped in the spatial domain (Figure 4.5B). The average spatial overlap index was 0.78 ±
Figure 4.5 Temporal and spatial offsets between excitation and inhibition. A, Excitatory and inhibitory
responses to preferred and null directions in two example cells. Scale: 20 / 58 pA (Exc); 46 /104 pA (Inh), 450
ms. Blue and red arrowheads mark peak response times. B, Excitatory and inhibitory receptive fields mapped
with flashing bars in the same cells as shown in A. Response traces were aligned according to the corresponding
bar position. The preferred direction of the cell is marked by the black arrow. Red and blue curves represent
skew-normal fitting of the receptive fields. Arrowheads mark their respective peaks. The skewness values are
indicated. Scale: 10 / 21 pA (Exc); 20 / 40 pA (Inh), 450 ms. C, Latencies of peak excitation (red) and peak
inhibition (blue) relative to the response onset at preferred (p = 1.8e-4, paired t-test, n = 12) and null (p = 1.2e-
5, paired t-test, n = 12) directions. D, Time intervals from response onset to response peak for excitatory
responses at preferred and null directions (p = 2.7e-6, paired t-test, n = 21). Data from the same cell are
connected. E, Time intervals from response onset to response peak for inhibitory responses (p = 0.064, two-
tailed paired t-test, n = 12). F, Overlap indices between spatial tuning of excitatory and inhibitory responses.
Solid symbols represent mean ± SD. G, DSI of excitatory input versus skewness of excitatory receptive field (n
= 16). Dash line is the best-fit linear regression lines. The correlation coefficient r is indicated. H, Skewness of
inhibitory receptive field versus that of excitatory receptive field. Inset, average skewness (red for excitation,
blue for inhibition, p = 6e-4, paired t-test, n = 12). Bar = SEM. I, Location of receptive field peak relative to the
preferred side of the cell for excitation and inhibition (p = 1.8e-4, paired t-test, n = 12). J, Onset latency of each
flashing-bar evoked excitatory response versus its peak amplitude (normalized to the maximum value in the
same cell). Each color represents one cell. The best-fit linear regression lines are shown. Inset, onset latencies
of excitatory responses evoked by flashing bars at different positions in an example cell. K, Onset latency of
each flashing-bar evoked inhibitory response versus its relative peak amplitude. L, Plot of estimated time for a
bar to move from the receptive field boundary to the receptive field peak against the observed time interval
between the onset and peak of excitatory responses to preferred (solid) and null (open) directional movements.
Dash line is the identity line. M, Plot in the same way as L for inhibitory responses.
78

0.08 (n = 12, Figure 4.5F). Second, as shown by the average response traces of two example
cells, the spatial distribution of peak amplitudes of flash-bar evoked excitatory responses was
skewed toward the preferred side, manifested by a longer tail of the spatial tuning curve on the
right than the left side (Figure 4.5B, the red curve). A summary of 16 cells showed a strong
positive correlation between the skewness of excitatory receptive field and DSI of moving-bar
evoked excitatory responses (Figure 4.5G). In addition, the excitatory receptive field was
always skewed toward the side consistent with the cell’s preferred direction, as manifested by
positive skewness values (Figure 4.5G). Together the results indicate that the stronger the
receptive field skewness the more selective are the moving-bar evoked excitatory responses.
Third, the inhibitory receptive field was much more spatially symmetric compared to the
excitatory receptive field in the same cell, as shown by the close-to-zero skewness value
(Figure 4.5H). This result is also consistent with the observation that inhibitory responses to
preferred and null directional movements had a similar peak time (Figure 4.5E). Due to the
differential spatial distribution of excitatory and inhibitory receptive fields, the inhibitory
receptive field peak was displaced toward the null side of the cell relative to its excitatory
counterpart (Figure 4.5B, 4.5I). This spatial offset between excitatory and inhibitory receptive
field peaks (3.8 ± 2.3º , n =12) was small compared to the overall receptive field sizes (40.6 ±
10.3º , n = 12), but was highly significant (p < 0.001, paired t-test).
The temporal offset between peak excitation and inhibition evoked by a moving stimulus
(Figure 4.5C) is consistent with the spatial offset between peak excitation and inhibition
evoked by stationary (flash) stimuli, suggesting that the spatial relationship has been translated
into a matching temporal relationship. To further demonstrate this point, we predicted the
79

timing of peak excitation/inhibition under moving stimuli based on the spatial receptive field
property. We first analyzed the onset latency of each flashing-bar evoked synaptic response.
We found that in general the latency correlated negatively with the peak response amplitude
(Figure 4.5J, 4.5K), so that the latency at the receptive field peak (where the largest response
was evoked) was shorter than that at receptive field peripheries (Figure 4.5J, 4.5K, inset).
Taking the onset latencies of flashing-bar responses and the moving bar speed into
consideration, the predicted timing of peak synaptic response generally matched the
experimentally observed value (Figure 4.5L, 4.5M), indicating that the timing of peak
moving-bar evoked response reflected the time when the receptive field peak was stimulated.
4.3.7 Spatiotemporal offsets between excitation and inhibition contribute to
direction selectivity
To better understand how receptive field organization might contribute to direction selectivity,
we performed simulations with a conductance-based neuron model (Zhang et al., 2003). The
moving-bar evoked response was simulated as a sum of responses to sequential flashing bars
evenly spaced in time (see Methods). For simplicity, the modeled flashing-bar responses had
similar temporal profiles except that their onset delays depended on their response amplitudes
in a linear fashion (Figure 4.6A). The latter then depended on bar locations within the receptive
field. To be consistent with our experimental observation that the average onset latency
difference between flash-stimulus evoked excitation and inhibition was 18 ms (Figure 4.6B),
in our model each flashing-bar evoked inhibitory response was delayed relative to the
excitatory response by 18 ms (∆T = 18 ms). After summing up the individual-bar evoked
responses (Figure 4.6C, upper panel), the resulting moving-bar evoked excitatory and
80

inhibitory conductances were fed into the neuron model to derive the expected Vm response
(Figure 4.6C, lower panel). DSI was calculated from the peak Vm responses to the preferred
and null directions.
The modeled excitatory and inhibitory receptive fields completely overlapped (Figure 4.6D,
inset). We varied the skewness of the excitatory receptive field while keeping the inhibitory
receptive field symmetric (Figure 4.6D, inset). This generated a varying spatial offset between
excitatory and inhibitory receptive field peaks (∆X_peak). When both the excitatory and
0 5 10 15
0.0
0.1
0.2
0.3
DSI_Vm
X_peak (º )

T = 0 ms
T = 18 ms
0 200 400
0.0
0.5
1.0
1.5
Time (ms)
Amplitude (nS)
F E D
A B
0
2
4
0 1 2
-70
-60
Time (s)
Vm (mV)
G (nS)
Exc
Inh
0 10 20 30
0
10
20
30
∆ T (ms) (Inh-Exc)
Percentage (%)
C
0 25 50
0.0
0.2
0.4
T (ms)
DSI_Vm
0.0 0.5 1.0
0.0
0.1
0.2
0.3
Exc
Vm
DSI
Exc
Inh

Exc
Inh
Skewness
Flash-bar responses
Moving-bar responses
Figure 4.6 Spatiotemporal offsets between excitation and inhibition contribute to direction selectivity. A,
Temporal profiles of simulated individual flashing-bar evoked excitatory responses. The onset latency of each
response is negatively correlated with its amplitude (see Methods). Note that the absolute amplitudes are not
important. B, Distribution of onset latency differences between excitation and inhibition evoked by flash stimuli.
C, Top, excitatory and inhibitory synaptic conductances in response to a drifting bar, generated by the
summation of individual flash-bar evoked responses. Bottom, membrane potential response derived by
integrating the above synaptic conductances in the neuron model. D, DSI of derived membrane potential
response plotted against the spatial offset between excitatory and inhibitory receptive field peaks. The delay of
flash-bar evoked inhibition relative to the excitation was set at 18 ms (black) or 0 ms (gray). Inset, schematic
illustration of varying the skewness of excitatory receptive field (red curve) while keeping the inhibitory
receptive field (blue curve) symmetric. Black arrow indicates the preferred direction of the cell. E, DSI of
excitatory and Vm responses plotted against the skewness of synaptic receptive fields. Inset, illustration of co-
varying the skewness of excitatory and inhibitory receptive fields. F, DSI of derived membrane potential
response as a function of latency of flash-bar evoked inhibition relative to excitation.
81

inhibitory receptive fields were symmetric (i.e. ∆X_peak = 0), the Vm response was not
direction tuned (DSI_Vm = 0) (Figure 4.6D, black). As the excitatory receptive field became
skewed and ∆X_peak increased, DSI of Vm response quickly increased and then declined
(Figure 4.6D, black), suggesting that a small spatial offset is sufficient and optimal for
producing tuned output response. The positive value of DSI indicates that the preferred side is
exactly the side toward which the excitatory receptive field is skewed, which is consistent with
our experimental observation (Figure 4.5G). Therefore, a skewed excitatory receptive field
plus non-skewed inhibitory receptive field can lead to correct directionality of output response.
The receptive field skewness per se however does not directly result in direction-tuned synaptic
responses, as the simulated moving-bar evoked excitatory response was not direction-tuned
(Figure 4.6E, red). In addition, when excitatory and inhibitory receptive fields were both
skewed but without a spatial offset (∆X_peak = 0), no direction-tuned output response was
generated (Figure 4.6E, black). Therefore the effect shown in Figure 4.6D could only be
attributed to a result of the spatial offset between excitation and inhibition.
The generation of direction tuned output response by the spatial offset between excitatory and
inhibitory receptive fields depended on the temporal relationship between flashing-stimuli
evoked excitatory and inhibitory responses. When the flash-bar evoked inhibitory response had
the same onset delay as the excitatory response (i.e. ∆T = 0 ms), direction-tuned output
responses failed to be generated at an optimal spatial offset (Figure 4.6D, grey). We
systematically varied ∆T under a fixed spatial relationship between excitatory and inhibitory
receptive fields. We found that only when the flash-bar evoked inhibition was delayed relative
to excitation (i.e. ∆T > 0 ms), were correctly tuned output responses generated (Figure 4.6F).
82

Therefore, the contribution of excitation-inhibition spatial offset to direction selectivity relies
on an appropriate temporal delay of stationary-stimulus evoked inhibition relative to
excitation.
Discussion
In cats and monkeys, cortical direction selectivity appears to be generated de novo, because
few, if any, thalamic relay cells are found to be direction selective (Wiesel and Hubel, 1966;
Cleland and Levick, 1974; Dreher et al., 1976). Simple cells in the cortical input layer are the
first stage where direction selectivity is created, and direction-selective simple cells then
provide tuned input to direction-selective complex cells (Priebe et al., 2010). The selectivity
of simple cells can be attributed to a progressive change of time course of excitatory responses
across the receptive field, which would show a slant when plotted in space and time coordinates
(Reid et al., 1987, 1991; DeAngelis et al., 1993; Jagadeesh et al., 1993; Jagadeesh et al., 1997).
An inhibitory mechanism for direction selectivity on the other hand had been more
controversial. Very early studies in cats showed that pharmacologically blocking cortical
inhibition resulted in a reduction of direction selectivity in simple cells (Sillito, 1975; Nelson
et al., 1994). However, this has now been shown to be an indirect effect of changes in the cell’s
excitability (Katzner et al., 2011). More recent intracellular recordings have revealed that both
excitation and inhibition to simple cells are well tuned for the same direction, attributable to
the slanted spatiotemporal organization of inputs (Priebe and Ferster, 2005). In addition,
because excitation and inhibition are separated in space, they are temporally out of synchrony
under moving stimuli of preferred orientation, resulting in a push-pull relationship between
excitation and inhibition (Hirsch and Martinez, 2006). As such, the temporal separation
83

between excitation and inhibition determines that inhibition does not contribute to sharpening
of response selectivity (Priebe and Ferster, 2005, 2008).
In the mouse, previous studies suggest that the mechanisms for cortical direction selectivity
could potentially be different from that in the cat. For example, a small subset of dLGN neurons
(about 10%) are already direction selective (Marshel et al., 2012). While it remains to be
determined where these direction selective dLGN neurons project to, they are in principle
capable of directly providing direction-tuned excitatory input into the cortex (Piscopo et al.,
2013). This notion seems to be supported by a developmental study showing that direction
selective cortical neurons in the mouse V1 already exist right after eye opening and that there
are remarkable functional similarities between the development of direction selectivity in
cortical neurons and that in the mouse retina (Rochefort et al., 2011). More importantly,
intracellular recording studies have suggested that there is a large spatial overlap between
excitation and inhibition to mouse V1 neurons even for simple cells (Liu et al., 2010; Liu et
al., 2011; Tan et al., 2011). As a consequence, under moving stimuli it is possible to evoke
inhibition that is temporally overlapping with excitation, allowing inhibition to contribute to
response selectivity by differentially suppressing excitation.
In this study, we directly examined excitatory and inhibitory inputs underlying the direction
selectivity of layer 4 simple cells in the mouse V1. Similar as observed in the cat (Priebe and
Ferster, 2005), we found that excitatory inputs are direction tuned and the preferred direction
of excitatory input is the same as that of membrane potential response, indicating that
excitatory input provides the seed for direction selectivity. However, different from cat simple
cells, the tuning of excitatory input cannot be attributed to a unidirectional shift of input
84

latencies across different receptive field locations, as the latencies are organized into two
slopes within the synaptic receptive field (Figure 4.5J). Our modeling results further
demonstrate that a linear summation of excitatory inputs with the observed spatiotemporal
organization fails to produce correct direction tuning under moving stimuli (Figure 4.6E),
suggesting that nonlinear mechanisms at thalamocortical synapses or upstream stages are
responsible for the tuning of excitatory input. Although tuned, the excitatory input only
exhibits moderate selectivity. Due to the nonlinear filtering properties, in particular a saturating
input-output transfer function, of the cell membrane (Liu et al., 2011), the excitatory input
alone would result in even more weakly tuned membrane potential response (Figure 4.4C).
Fortunately, due to the spatial overlap between excitation and inhibition, moving stimuli evoke
inhibition that also overlaps with excitation in the temporal domain. The close temporal
interaction of inhibition with excitation leads to a sharpening of direction selectivity of
membrane potential responses, allowing the selectivity in excitatory inputs to be fully
expressed in membrane potential responses (Figure 4.4C). Eventually, spike threshold further
exerts a strong sharpening effect, leading to sharply tuned output responses (Figure 4.2B). Our
finding of inhibitory sharpening of direction selectivity is consistent with a recent study
showing that reducing visually evoked inhibition by only 10% via optogenetic inactivation of
PV inhibitory neurons results in a moderate but significant decrease in direction selectivity of
cortical responses (Atallah et al., 2012).
Two specific mechanisms contribute to the inhibitory sharpening of direction selectivity of
output responses. First, excitation is direction-tuned while inhibition is largely untuned,
resulting in relatively stronger inhibition under null directional movements compared to
85

preferred directional movements. The relatively untuned inhibition is attributable to
unselective output responses of inhibitory neurons (Figure 4.1B). Second, while excitatory
and inhibitory receptive fields are spatially overlapping, the excitatory receptive field is
skewed toward the preferred side of the cell while the inhibitory receptive field is more or less
spatially symmetric. These differential spatial distributions of excitatory and inhibitory inputs
are translated into differential temporal offsets between peak excitatory and inhibitory
responses evoked by moving stimuli of opposite directions: the peak excitation precedes the
peak inhibition under preferred directional movements, whereas it is more delayed than the
latter under null directional movements. Such specific temporal relationships between
excitation and inhibition facilitate a more effective inhibitory suppression of the membrane
potential response to the null direction than the preferred, and are reminiscent of previous
studies in somatosensory and auditory cortices showing that the temporal overlap between
excitation and inhibition is different for opposite directional stimuli (Zhang et al., 2003; Wilent
and Contreras, 2005). Interestingly, our modeling results demonstrate that a skewed
distribution of excitatory inputs plus symmetric distribution of inhibitory inputs is sufficient to
result in direction-tuned membrane potential responses even if none of the synaptic responses
per se is tuned (Figure 4.6D), providing that stationary-stimulus evoked inhibition is
temporally delayed relative to excitation. These results indicate that the differential spatial
tuning of excitation and inhibition is an important factor contributing to the inhibitory
sharpening of direction selectivity.
Together, our data suggest that the synaptic mechanisms for direction selectivity in mouse
simple cells are distinct from simple cells in carnivores in several respects. Direction selectivity
86

of cat simple cells originates from a unidirectional shift of response latencies across the RF,
whereas direction selectivity of mouse simple cells likely originates from direction-tuned
responses in the retina. Excitation and inhibition are organized in a push-pull pattern in cat
simple cells, whereas they overlap both spatially and temporally in mouse simple cells.
Inhibition does not contribute to direction selectivity in cat simple cells, whereas it contributes
importantly to sharpening of direction selectivity in mouse simple cells. These differences may
reflect divergent evolutionary solutions to generating direction selectivity in visual cortex. The
observed spatial and temporal overlap of excitation and inhibition in mouse simple cells
resonates with recent theoretical models of cortical signal processing where excitatory and
inhibitory inputs are dynamically correlated (Vogels and Abbott, 2009; Kremkow et al., 2010).
Our study also raises several interesting questions to be investigated in the future. For example,
how do skewed excitatory receptive fields arise while inhibitory receptive fields are all
symmetric? Previous experimental and modeling studies have demonstrated that repeated
directional stimuli can induce an asymmetric shaping of cortical synaptic circuits through
spike-timing dependent plasticity (STDP) (Mehta et al., 2000; Rao and Sejnowski, 2000;
Engert et al., 2002; Fu et al., 2004; Wenisch et al., 2005). These results suggest that asymmetric
receptive fields may arise through activity-dependent mechanisms. Since the development of
direction selectivity in mouse V1 neurons is not affected by rearing animals in darkness
(Rochefort et al., 2011), it is possible that some endogenously generated activity waves are
sufficient to drive the formation of asymmetric excitatory receptive fields. Likely, some
directional bias provided by retinal/thalamic input exists to facilitate symmetry-breaking under
activity waves of all different directions (Li et al., 2008), which may explain why the excitatory
87

receptive field is always skewed toward the preferred side and why not all excitatory cells have
asymmetric receptive fields. Noticeably the STDP-dependent asymmetric modification of
synaptic circuits only applies to excitatory connections, whereas the activity-dependent
plasticity of inhibitory connections or that of excitatory synapses onto inhibitory neurons is not
sensitive to the temporal order of pre- and postsynaptic spiking (Bi and Poo, 2001; Woodin et
al., 2003; Lu et al., 2007). This may explain why asymmetric inhibitory receptive fields do not
develop. Finally, the nonselective property of inhibitory neurons well explains the untuned
inhibitory input, but also raises the question of why they are different from excitatory neurons.
Do these neurons all receive untuned excitatory inputs, or do their inhibitory inputs display
properties that prevent them from effectively suppressing the null response? Future
intracellular recordings from inhibitory neurons are required to address this question.

88

Chapter 5: Synaptic circuits for orientation selectivity
during development in layer 4
Introduction
The development of orientation selectivity, a fundamental functional property of visual cortical
neurons (Hubel and Wiesel, 1962; Ferster and Miller, 2000), has been extensively
characterized in previous studies, especially at the level of extracellular spike signals. In many
species other than primates (Wiesel and Hubel, 1974), orientation selectivity matures over an
extended period of postnatal life. In the cat, maturation of orientation tuning occurs in the first
few weeks after birth (Hubel and Wiesel, 1963; Barlow and Pettigrew, 1971; Blakemore and
Van Sluyters, 1975; Buisseret and Imbert, 1976; Fregnac and Imbert, 1978; Tsumoto and Suda,
1982; Albus and Wolf, 1984). In the ferret and rodent, it has been shown that orientation
selectivity progressively matures within weeks after the time of natural eye opening, with the
percentage of orientation-selective neurons gradually increased and the sharpness of
orientation tuning enhanced (Chapman and Stryker, 1993; Fagiolini et al., 1994; Kuhlman et
al., 2011; Rochefort et al., 2011).

In parallel with the maturation of single-cell orientation
selectivity, orientation maps in the ferret as detected by intrinsic optical imaging also
strengthen and become high-contrast (Chapman et al., 1996; Godecke et al., 1997; White et
al., 2001).
The previous extracellular recording and imaging studies on the development of orientation
selectivity only examined spike outputs or output-related signals. The cellular and synaptic
89

mechanisms underlying the developmental sharpening of orientation selectivity remain yet
uncertain. Theoretical work with activity-instructed, correlation-based models have shown that
orientation selectivity can arise from synaptic strengthening and elimination of excitatory
neuronal connections, in particular geniculocortical connections (Miller, 1992; Miyashita and
Tanaka, 1992; Miller, 1994). Orientation tuned cortical inhibitory neurons would also appear
with these changes in excitatory connectivity (Kayser and Miller, 2002). These results have
suggested that a refinement of excitatory circuits is a major driving force for the sharpening of
orientation selectivity. However, it is possible that changes of both excitatory and inhibitory
inputs can contribute significantly to the development of orientation selectivity, based on the
results of many studies in adult sensory cortices showing that the excitatory input usually
determines the tuning preference, while inhibition sharpens the tuning selectivity of output
responses (Ben-Yishai et al., 1995; Somers et al., 1995; Troyer et al., 1998; Wehr and Zador,
2003; Zhang et al., 2003; Tan et al., 2004; Marino et al., 2005; Wu et al., 2008; Liu et al.,
2011). During development, both the amplitude and tuning profile of excitatory and inhibitory
synaptic inputs may undergo drastic changes. If we ignore detailed changes in synaptic
strength, in principle two prominent mechanisms can account for the orientation selectivity
sharpening (Figure 5.1A). First, the tuning profile of excitatory inputs is sharpened during
development. Second, the tuning profile of inhibitory inputs is broadened, which can also
effectively enhance tuning selectivity (Somers et al., 1995; Lauritzen and Miller, 2003;
Shapley et al., 2003; Wu et al., 2008; Liu et al., 2011). In this study, we have carried out in
vivo whole-cell voltage-clamp recordings from layer 4 excitatory neurons in the mouse V1 to
distinguish these potential synaptic mechanisms.
90

Methods
5.2.1 Animal preparation
Animal was prepared as described in 3.2.1. Female C57BL/6 mice from P14 to P90 were used.
The anaesthesia and sedative for pups younger than P20 was dilated to 10% of that used for
adults.
5.2.2 In vivo electrophysiology
The blind recording was performed as described in 4.2.2. All neurons recorded in this study
were located at a depth of 375-520µ m below the pia according to the microdrive reading,
corresponding to layer 4. According to previous results, the majority of layer 4 excitatory cells
are simple cells based on response modulation (Niell and Stryker, 2008) and spiking receptive
field structure ((Liu et al., 2009), note that monocontrast cells with one subfield can be
considered as simple).
5.2.3 Visual stimulation
The visual stimulation was set up as described in 2.2.5. The drifting sinusoidal gratings as
described in 2.2.5 were applied to measure the orientation selectivity.
5.2.4 Data analysis
The data acquisition and analysis of spike responses is the same as described in 2.2.7. For
measuring the standard OSI, the response levels for drifting sinusoidal gratings of two
directions at the same orientation were averaged to obtain the orientation tuning curve between
0-180 degrees, which was then fit with a Gaussian function R( )=A*exp(-0.5*( - )
2
/ 
2
)+B.
91

 is the preferred orientation. The standard OSI was quantified as (Rpref – Rorth)/(Rpref + Rorth)
= A/(A+2*B), where Rpref is the response level at the angle of φ, and Rorth is that at the angle
of +90º .
Excitatory and inhibitory conduces were derived as described in 3.2.4. The peak conductance
as well as the integral was quantified. Orientation tuning of synaptic conductance was analyzed
in a similar way as described above.
5.2.5 Modelling
A conductance-based integrate-and-fire neuron model as described in 3.2.4 was used to
simulate the membrane potential response. Synaptic conductance evoked by a moving grating
was simulated by fitting the average waveform of synaptic response with a skew normal
distribution function (Azzalini, 1985), which yielded a better fit than sinusoidal functions or
alpha functions:

 and Φ are the standard normal probability density function and the cumulative distribution
function respectively. ξ determines the location. The scale factor (ω) was set at 145 ms and the
shape factor (α) at 1.5 for both excitatory and inhibitory conductances. The synaptic
conductance was set as 0 if it was smaller than 10% of maximum, as to exclude a very slow
rising phase. We quantified the phase difference between excitation and inhibition for
optimally evoked responses. It was 17º ± 14º at ST1 (n = 13), 22º ± 15º at ST2 (n = 14), and
there was no significant difference between ST1 and ST2 (p > 0.1, t-test). Based on these
experimental observations, inhibition was set with a 25 ms delay relative to that of excitation
baseline
x x
amplitude x f 

 

  )) ( ( ) (
2
) (







92

(corresponding to an 18º phase difference). Varying the relative delay of inhibition from 5ms
to 50ms did not affect our conclusion (data not shown). To simulate responses to different
orientations, the shape of evoked synaptic conductance did not change but only the peak
amplitude varied according to the tuning curves created based on the average OSI in our
experimental data (Figure 5.3B). The peak amplitudes of Ge and Gi in the first cycle were
based on average experimental data (1.63 nS for excitation and 2.78 nS for inhibition at ST1;
2.76 nS and 5.62 nS at ST2). The peak amplitudes in the second and third cycle were
determined by the adaptation factor assigned.
5.2.6 Dynamic clamp
Dynamic clamp recordings were carried out according to (Nagtegaal and Borst, 2010). The
current injected into the cell was calculated in real time by a custom-written LabVIEW routine
and controlled by National Instrument Interface:

The time-dependent Ge and Gi were similar as shown in Figure 5.3A. Ee and Ei were set as
0mV and -70 mV respectively. The membrane potential Vm was sampled at 5 kHz. The
junction potential was corrected. Measurements of Vm were corrected off-line for the voltage
drop on the uncompensated, residual series resistance (15-20 MΩ). The corrected Vm was only
slightly different from the recorded Vm.

   
i m i e m e
E t V t G E t V t G t I       ) ( ) ( ) ( ) ( ) (
93

Results
5.3.1 Development of orientation selectivity in layer 4 excitatory neurons of
mouse visual cortex
To first understand the progression of the developmental sharpening of orientation selectivity
in the mouse visual cortex, we carried out in vivo blind single-cell loose-patch recordings in
layer 4 (see Methods). Our recording method resulted in highly biased samplings from
excitatory neurons (Liu et al., 2009; Liu et al., 2010; Ma et al., 2010), as evidenced by the
broad spike waveforms observed in all the recorded cells. The cells were pooled into three age
groups: within 5 days after the time of eye opening (ST1, ~P15 – P19), during the critical
period for ocular dominance plasticity (ST2, P21 – P30), and in adulthood (P70 - P100). As
shown by the post-stimulus spike-time histograms (PSTHs) for the responses of example cells
to drifting sinusoidal gratings (Figure 5.1B), orientation tuning of spiking responses was
usually broad at ST1, while sharp tuning appeared in a significant number of cells at ST2. We
used gOSI to quantify the degree of orientation selectivity, which is a good single metric for
orientation selectivity that takes into account responses to all orientations (Ringach et al.,
2002). The cumulative distribution of gOSIs shifted rightward from ST1 to ST2, indicating
that orientation selectivity is developmentally sharpened (Figure 5.1C). The mean gOSI (±
SD) was 0.27 ± 0.21 at ST1, 0.44 ± 0.22 at ST2, and 0.42 ± 0.22 in adulthood. The distribution
of gOSIs was not different between ST2 and adulthood (Figure 5.1C), indicating that the
maturation of orientation selectivity in the mouse largely occurs during ST1. Indeed, when we
further divided ST1 into two substages, within 2 days after eye-opening and 3-5 days after eye-
opening, we found that there was a marked difference in gOSI between the substages (from
94

0.22 ± 0.17 to 0.35 ± 0.23, mean ± SD, n = 11 and 14 respectively, p < 0.05, t-test). This
indication of a fast maturation of orientation selectivity within days after eye opening is
consistent with the results of a previous report (Wang et al., 2010). Matching with the

Exc Inh
ST1
0
2
4

0.0
0.5
1.0

Exc Inh
0.4
0.6
0.8
1.0
120 30 210
Norm. G
θ (º )
ST2
Exc
Inh
0.0 0.5 1.0
0
50
100
Adults
ST1
ST2
Global OSI
Cumulative percentage
1ms
C
Norm. spike #
B
0.0
0.5
1.0
0 180

90
θ (º )
ST1
0.0
0.5
1.0
-30 150

60
θ (º )
ST2
Δ Inh Δ Exc Δ Inh Δ Exc
A
E
Exc
Inh
0.4
0.6
0.8
1.0
120 30 210
Norm. G
θ (º )
F
D
ST1 ST2
Evoked FR (Hz)
**
* Pref
Orth
Spontaneous FR (Hz)
Spon
*
0.4
0.6
0.8
1.0
120 30 210
θ (º )
First cycle Cycle averaged
0.4
0.6
0.8
1.0
120 30 210
First cycle Cycle averaged
θ (º )
95

measurement of gOSI, the tuning width (see Methods) was reduced from ST1 to ST2 (from
39.2 ± 17.4º to 27.2 ± 17.8º , mean ± SD, p < 0.01, t-test). The sharpening of orientation tuning
can be attributed to both an increase of spike rate in response to optimal orientation and
importantly a decrease of spike rate to orthogonal orientation (Figure 5.1D). Together with the
fact that the average spontaneous firing rate was reduced during the same period (Figure 5.1D),
it appears that there is a general increase in inhibitory tone in the cortex during development
(Morales et al., 2002; Chattopadhyaya et al., 2004). Based on the developmental progression
of spiking response selectivity, we chose to compare excitatory and inhibitory synaptic tuning
profiles between ST1 and ST2.
5.3.2 Synaptic inputs underlying orientation selectivity during development
To dissect visually evoked excitatory and inhibitory inputs, we carried out in vivo whole-cell
voltage-clamp recordings (see Methods). We recorded synaptic responses to drifting gratings
when clamping the cell’s membrane potential at two different levels, -70mV and 0mV (Liu et
Figure 5.1 Developmental sharpening of orientation selectivity in mouse visual cortex. A, Two potential
synaptic models for the developmental sharpening of OS. Black and red curves represent the orientation tuning
curve of excitatory (Exc) and inhibitory (Inh) inputs, respectively. Increasing tuning selectivity of excitation
(bottom left) or decreasing tuning selectivity of inhibition (bottom right) can both lead to a sharpening of
selectivity of output responses. B, Two example layer 4 excitatory neurons at stages indicated. Plots are
poststimulus spike time histograms for their responses to drifting sinusoidal gratings at various directions
(indicated on the left). Bottom, The normalized orientation tuning curve of the spike count and the corresponding
Gaussian fit. Calibration: 12 Hz (left) and 18 Hz (right) and 150 ms. Blue arrows point to the optimal orientation.
C, Cumulative distribution of gOSI for layer 4 excitatory neurons in different age groups. ST1, n25; ST2, n17;
adult, n42. Inset, Superimposed individual spike waveforms for an example excitatory neuron. Note that the
trough-to-peak interval is longer than 0.5 ms. ST1 differs significantly from ST2 and adult (p 0.05, Mann–
Whitney test). ST2 does not differ from adult (p = 0.56, Mann–Whitney test). D, Average evoked firing rate (FR)
for preferred (dark) and orthogonal (white) orientations, as well as the average spontaneous firing rate (hatch) of
excitatory neurons at ST1 (n = 25) and ST2 (n = 17). Error bars indicate SEM. *p = 0.05; **p = 0.01; t-test. E,
Average excitatory and inhibitory synaptic currents evoked by drifting gratings at various directions for an
example layer 4 excitatory neuron (at P17). Stimulus cycles are labeled on top. Boxed are cycle-averaged response
waveforms. Calibration: 100 (Exc)/140 (Inh) pA, 200 ms. Bottom, Normalized orientation tuning curves of peak
excitatory (black) and inhibitory (red) conductances of the cycle-averaged waveform (left) or of the first cycle
(right) and the corresponding Gaussian fits. F, A sample cell at P24. Data presentation is the same as in E.
Calibration: 200 (Exc)/275 (Inh) pA, 200 ms.
96

al., 2010). In all the cells, excitatory and inhibitory synaptic responses were observed at all
testing orientations (Figure 5.1E). In many cases excitatory and inhibitory responses showed
an evident adaptation, i.e. reducing response level with increasing stimulus cycles (Figure
5.1E), although there were cases where the maximum conductance occurred during the later
cycles. Interestingly, adaptation was much less obvious in spiking responses (Figure 5.1B).
Two measurements were applied to quantify the level of synaptic responses. In the first, we
followed the conventional way to average the synaptic responses by cycles, and then measured
the peak conductance after smoothing the response curve with a 40ms sliding window for
averaging. In the second, we measured the peak conductance in the first cycle of smoothed
responses. Values of these two measures were used to plot the synaptic tuning curve. In the
example P17 cell, excitation and inhibition appeared to have a similar preferred orientation,
and the inhibitory tuning appeared slightly narrower than excitation (Figure 5.1E). In
comparison, in the example P24 cell, while the excitatory tuning was similar as that in the P17
cell, the inhibitory tuning appeared much broader and was broader than the excitatory tuning
in both measures (Figure 5.1F).
The trend of a broadening of inhibition was evident in responses of many other cells (Figure
5.2A, 5.2B). Data were summarized for a total of 13 ST1 cells and 14 ST2 cells. Analysis of
peak conductance of cycle-averaged waveforms showed that at ST1, the tuning selectivity of
inhibition was slightly but significantly higher than that of excitation (p < 0.01, paired t-test,
Figure 5.2C, left). From ST1 to ST2, there was no change in tuning selectivity of excitation
(p = 0.32, t-test), while that of inhibition was greatly reduced (p < 0.001, t-test) and became
significantly lower than that of excitation (p < 0.001, paired t-test, Figure 5.2C, left).
97

0.0
0.1
0.2

0
5
10
0
1
2

0 90 180
0
90
180
ST1
ST2
ST1 ST2
C
Global OSI
D
Exc θ
pref
(º )
Inh θ
pref
(º )
Exc
Inh
E
ST1 ST2
E/I ratio
Exc
Inh
Peak G (nS)
Norm. G
θ (º )
Peak G, cycle-averaged
ST1 ST2
0.5
1.0
-90 0 90
0.5
1.0
120 30 210
0.5
1.0
90 0 180
Norm. G
θ (º )
0.5
1.0
120 30 210
0.5
1.0
0 180

90
Norm. G
θ (º )
0.5
1.0
60 240 150
0.5
1.0
60 240 150
ST1 ST2
Cell #2 Cell #3 Cell #4 Cell #5 Cell #15 Cell #16 Cell #17 Cell #18
B A
E I E I E I E I E I E I E I E I
F
0.5
1.0
-90 0 90
0.0
0.1
0.2

Peak G, first cycle
**
**
***
***
**
**
*
-90 0 90
0.6
0.8
1.0
θ (º )
-90 0 90
0.6
0.8
1.0
Cycle averaged First cycle
ST2, Gi
ST2, Ge
ST1, Gi
ST1, Ge
98

Essentially the same conclusion could be made when the integral conductance was measured
(data not shown), and when the peak conductance in the first cycle was measured except that
at ST1 inhibitory selectivity was not significantly different from excitatory selectivity (Figure
5.2C, right). Thus, the orientation tuning profile of excitatory inputs remained largely
unchanged, while that of inhibitory inputs became less selective. The preferred orientation of
inhibition was largely identical to that of excitation (Figure 5.2D), and their difference (Δθpref)
was on average 18.4 ± 8.8º (mean ± SD) at ST1 and 20.5 ± 7.9º at ST2 (p = 0.64, t-test). To
further reveal the developmental changes in synaptic tuning profiles, we averaged normalized
synaptic tuning curves of all the cells, which were aligned according to the optimal orientation
(Figure 5.2E). The average inhibitory tuning curve was similar as or slightly narrower than
the average excitatory tuning curve at ST1, but became significantly broader than the
excitatory tuning at ST2 (Figure 5.2E). The average tuning width of excitation was 36.3 ±
15.1º at ST1 and 35.7 ± 11.3º at ST2 (mean ± SD, p > 0.4, t-test), while that of inhibition was
Figure 5.2 Synaptic inputs underlying orientation selectivity of developing layer 4 excitatory neurons. A,
Cycle-averaged excitatory (Exc/E) and inhibitory (Inh/I) responses for more layer 4 excitatory neurons at ST1.
Calibration: Cell 2, 32.6 (E)/50.2 (I) pA; Cell 3, 27.2 (E)/48.8 (I) pA; Cell 4, 32.2 (E)/44.5 (I) pA; Cell 5, 51.6
(E)/71.6 (I) pA, and 100 ms. Bottom, Synaptic tuning curves. Black, Excitation; red, inhibition. B, More example
layer 4 excitatory neurons at ST2. Calibration: Cell 15, 54.4 (E)/62.2 (I) pA; Cell 16, 74.6 (E)/89.4 (I) pA; Cell
17, 49.7 (E)/62.3 (I) pA; Cell 18, 43.8 (E)/63.6 (I) pA, and 100 ms. C, Distribution of gOSIs for excitation (black)
and inhibition (red) at ST1 (n13) and ST2 (n14). Left, Based on measurements of peak conductance of the cycle-
averaged response. Right, Based on peak conductance in the first cycle. Solid symbols represent mean± SD. Data
points from the same cell are connected with a line. *p=0.05; **p=0.01; ***p=0.001; paired t-test or t-test. There
is no significant difference in excitatory tuning selectivity between ST1 and ST2 (p>0.1). D, Plot of preferred
orientation angle of excitation versus that of inhibition. Red line is the identity line. There is no significant
difference in preferred angle between excitation and inhibition at ST1 and ST2 (p>0.1, t-test), based on
measurements of peak conductance of the cycle-averaged response or that of the first cycle (data not shown). E,
Average excitatory and inhibitory tuning profiles. Synaptic tuning curves for individual cells were normalized
and aligned according to the optimal orientation (set as 0° ) before averaging. Error bars indicate SEM. Excitatory
and inhibitory tuning curves were aligned independently. Because excitation and inhibition shared a similar
preferred orientation, the plotted average excitatory and inhibitory tuning curves were given the same optimal
orientation. Left, Based on peak conductance of the cycle-averaged response. Right, Based on peak conductance
in the first cycle. F, Peak excitatory (black) and inhibitory (red) conductances of cycle-averaged responses to
optimally oriented gratings (triangle), and their ratio (E/I ratio, circle). Solid symbols represent mean± SD.
*p=0.05; **p=0.01; t test. The E/I ratio was 0.65± 0.27 at ST1 and 0.53± 0.21 at ST2 (p>0.1) if based on peak
conductance in the first cycle. Norm., Normalized.
99

31.4 ± 7.5º at ST1 and 44.2 ± 16.6º at ST2 (p < 0.02, t-test) when the peak conductance of
cycle-averaged responses was measured. Similarly, tuning width of excitation was 37.8 ± 15.3º
at ST1 and 34.9 ± 11.1º at ST2 (p > 0.1), and that of inhibition was 36.6 ± 16.1º at ST1 and
47.6 ± 18.6º at ST2 (p < 0.02) when considering the peak conductance of the first cycle. The
data indicate that the developmentally reduced selectivity of inhibitory tuning cannot be merely
attributed to a general increase of untuned inhibitory activity, but also to a broadening of input.
Finally, we observed that the strengths of excitation and inhibition evoked by the optimal
orientation were both increased from ST1 to ST2 (Figure 5.2F). This developmental increase
in excitatory and inhibitory synaptic strength is consistent with previous in vitro results in
visual cortical slices (Blue and Parnavelas, 1983; Morales et al., 2002; Chattopadhyaya et al.,
2004). The fold increase was similar for excitation and inhibition, so that the overall E/I ratio
was not significantly changed (Figure 5.2F).
5.3.3 An inhibitory mechanism for the developmental sharpening of
orientation selectivity
The above results indicate two concurrent processes associated with the development of
orientation selectivity. First, there is a scaling up of the excitatory tuning curve by
strengthening excitatory responses to different orientations multiplicatively, as reflected by the
unchanged excitatory tuning profile (Figure 5.2E, black). Second, while inhibitory inputs are
also strengthened, the response to the orthogonal orientation is strengthened relatively more
than that to the preferred orientation, resulting in a broadening of the inhibitory tuning profile
(Figure 5.2E, red). To understand how the synaptic strengthening per se and the
developmental broadening of inhibitory tuning might contribute to the sharpening of
100

0.0
0.1
0.2

0
3
6
0.0 0.5 1.0 1.5
-60
-50
Exc
Inh
-90 0 90

A1T1
A1T2
A2T1
A2T2
A
-90 0 90
0.6
0.8
1.0
ST1, Ge
ST1, Gi
ST2, Ge
ST2, Gi
-90 0 90
0.6
0.8
1.0
A1T1
A1T2
A2T1
A2T2
-90 0 90
12
14
16
18
A1T1
A1T2
A2T1
A2T2
-90 0 90
0.6
0.8
1.0
A1T1
A1T2
A2T1
A2T2
G(nS) Vm (mV)
Norm. G
1
st
Cycle PSP (mV) 1
st
Cycle PSP (mV)
Norm. PSP Norm. PSP
B
C D
E F
t (s)
θ (º ) θ (º )
θ (º )
θ (º )
θ (º )

-67

d-clamp
Vm
G
I
G H
Global OSI
ST1 ST2
0 90 180
0
10
20
ST1
ST2
ST1 ST2
θ (º )
0 90 180
0
10
20
PSP (mV) PSP (mV)
*
10
16
101

orientation selectivity, we performed simulations of neuronal responses by employing a
simple conductance-based integrate-and-fire neuron model (Liu et al., 2011), using parameter
values (e.g. synaptic strength and synaptic tuning profile) observed in our experiments. We
simulated synaptic conductance waveforms with a skew normal function (Figure 5.3A, upper
panel), with the inhibitory response delayed relative to the excitatory response by 25ms (see
Methods). For simulating synaptic responses evoked by stimuli of multiple cycles, we
quantified an adaptation factor (i.e. the ratio of the peak response amplitude in a cycle to that
in the previous) for optimally evoked excitation and inhibition at two stages. For excitation, it
was 0.65 ± 0.17 at ST1 and 0.69 ± 0.19 at ST2 (mean ± SD). For inhibition, it was 0.53 ± 0.21
at ST1 and 0.6 ± 0.21 at ST2. Consistent with the previous report (Tan et al., 2011), adaptation
of inhibition was slightly stronger than that of excitation (p < 0.05 at both ST1 and ST2, paired
t-test). Since there was no significant difference in adaptation between ST1 and ST2 (p > 0.1
for both excitation and inhibition, t-test), we assigned an average adaptation factor of 0.67 to
excitation and 0.57 to inhibition. As shown in Figure 5.3A (bottom panel), although synaptic
Figure 5.3 The broadening of inhibitory tuning is a determinant synaptic mechanism underlying the
developmental sharpening of orientation selectivity. A, Top, Simulated excitatory (black) and inhibitory (red)
synaptic conductances evoked by a drifting grating. Three cycles are shown. Adaptation factor is 0.67 for
excitation and 0.57 for inhibition. Bottom, The V m response generated in the neuron model (solid blue) by
integrating the excitatory and inhibitory conductances shown on top. B, Orientation tuning curves of excitation
(black) and inhibition (red) at ST1 (dash) and ST2 (solid) applied in the model. Tuning curves are based on peak
conductance in the first cycle. C, Tuning curves of peak V m responses (in the first cycle) resulting from different
combinations of maximum synaptic amplitude and synaptic tuning profile. A1T2, the combination of excitatory
and inhibitory synaptic amplitudes (optimally evoked) at ST1 and tuning profiles at ST2. PSP, Postsynaptic
potential. D, Normalized tuning curves of V m response. E, Top, The digital dynamic clamp calculates the current
(I) injected into the cell based on the instantaneous V m and time-dependent synaptic conductances (G). Black and
red curves show the time courses of simulated excitatory and inhibitory conductances in an example experiment,
respectively. The V m response is a raw trace from the recorded cell whose resting membrane potential was -67
mV. Calibration: 5 mV, 200 ms. Bottom, Tuning curves for recorded peak V m responses (in the first cycle) in the
dynamic-clamp recording of the example cell. F, Average normalized tuning curves of Vm responses in dynamic-
clamp experiments (n = 4 cells). Error bars indicate SD. G, Membrane potential responses to gratings of 12
directions in two example cells under current-clamp recording. Boxed are cycle-averaged response waveforms.
Calibration: 20 mV left/ 22 mV right, 200 ms. Note that spikes were blocked. H, Distribution of gOSIs of Vm
responses at ST1 and ST2 (n = 9, 9 cells, respectively). *p<0.05, t-test.
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responses adapted substantially, the resulting membrane potential (Vm) responses showed
almost no adaptation. This modelling result provides an explanation for the less obvious
adaptation in spiking responses than synaptic responses. The synaptic tuning profiles in the
modelling were based on the average tuning curves in our experimental data. While the
excitatory tuning changed little from ST1 to ST2, the inhibitory tuning was broadened (Figure
5.3B). We applied different combinations of parameter values, for example, the combination
of the maximum synaptic amplitudes at ST1 and synaptic tuning profiles at ST2 was referred
to as A1T2. As shown in Figure 5.3C and 5.3D (dash blue), applying synaptic amplitudes and
tuning profiles at ST1 (A1T1) resulted in Vm responses (measured as peak response of the first
cycle) of a relatively flat tuning profile, suggesting that the spiking responses would be very
broadly tuned at this stage. Applying synaptic amplitudes and tuning profiles at ST2 (A2T2)
resulted in a much sharper Vm response tuning, with an increased response at the optimal
orientation and a reduced response at the orthogonal orientation (solid blue). This is consistent
with the experimental observations that ST1 cells fired more strongly at the orthogonal
orientation than ST2 cells and that the evoked firing rate at the optimal orientation was
developmentally increased (Figure 5.1D). The developmental sharpening of Vm responses
could only be attributed to the increased broadness of the inhibitory tuning, because only
increasing synaptic strengths while maintaining the initial tuning profiles (A2T1) resulted in
even reduced selectivity of Vm responses, whereas only increasing the broadness of inhibition
while maintaining maximum synaptic amplitudes at ST1 (A1T2) resulted in nearly similarly
sharpened Vm responses (Figure 5.3C and 5.3D, solid and dash magenta respectively).
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To confirm that a sharpening of Vm responses caused by the developmental broadening of
inhibitory tuning can indeed occur in real cells, we carried out dynamic clamp recording from
layer 4 neurons in vivo (see Methods). We injected simulated synaptic conductances of
different combinations between maximum amplitude and tuning profile, similar as in the
modelling. Consistent with the modelling results, applying ST2 synaptic tuning profiles
resulted in a sharper tuning of Vm response, no matter which group of synaptic amplitudes
were used (Figure 5.3E and 5.3F, solid blue and open magenta). Applying ST1 synaptic tuning
profiles resulted in a nearly flat Vm response tuning (Figure 5.3E and 5.3F, open blue and
solid magenta), indicating that inhibitory tuning being sharp may be detrimental to the outcome
of orientation tuning.
The neuron modelling and dynamic clamp results predict that Vm response tuning should also
be sharpened during development. We examined membrane potential responses in a subset of
excitatory cells under current-clamp recording (see Methods). Indeed, we found that the tuning
of Vm responses at ST2 was significantly sharper than that at ST1 (Figure 5.3G, 5.3H). On
the other hand, biophysical properties such as resting membrane potential (-57 ± 9 mV at ST1,
-59 ± 12 mV at ST2, mean ± SD, n = 10 and 11, p > 0.1, t-test) and spike threshold (20.3 ± 4.3
mV above the resting potential at ST1, 20.8 ± 4.4 mV at ST2, p > 0.1) did not change
significantly during the same developmental period. These results, together with the modelling
and dynamic clamp studies, further suggest that the broadening of inhibitory tuning is a major
driving force for the developmental sharpening of orientation selectivity of excitatory neuron
responses.

104

5.3.4 Excitation and inhibition after dark rearing
Previous studies in ferrets and rats have shown that the maturation of orientation selectivity is
dependent on visual experience (Chapman and Stryker, 1993; Fagiolini et al., 1994; White et
al., 2001). Two recent studies in layer 2/3 of the mouse visual cortex however demonstrate that
orientation selectivity sharpening proceeds rather normally when animals are deprived of
visual experience by dark rearing (Kuhlman et al., 2011; Rochefort et al., 2011). Consistent
with these recent results, we found that dark rearing starting from P9 did not prevent the normal
sharpening of orientation selectivity in layer 4 excitatory neurons (Figure 5.4A, black). The
tuning width of their spiking responses was 28.4 ± 13.6º (mean ± SD), not different from 27.2
± 17.8º at normal ST2 (p > 0.1, t=test). In addition, dark rearing did not prevent the broadening
of inhibition either (tuning width was 44.5 ± 9.5º , not different from that at normal ST2, p >
0.1), nor did it have any effect on the excitatory tuning selectivity (Figure 5.4B, 5.4C). The
same conclusion could be made when peak conductance in the first cycle was measured. In
this measure, the gOSI of inhibition was 0.04 ± 0.01 (mean ± SD) under dark rearing, different
from 0.09 ± 0.05 at ST1 (p < 0.01, t test), but not from 0.04 ± 0.02 at normal ST2. The gOSI
of excitation was 0.09 ± 0.05 under dark rearing, which was not different from 0.08 ± 0.03 at
ST1 or 0.08 ± 0.03 at normal ST2. However, dark rearing did have effects on synaptic strength:
it impeded the developmental strengthening of excitation, while the strengthening of inhibition
seemed unaffected (Figure 5.4D). Based on these results and our modelling study, we
conclude that the apparently “normal” sharpening of orientation selectivity in dark-reared
animals can still be attributed to a broadening of inhibitory tuning, although the underlying
synaptic circuits may have in fact been altered by dark rearing.
105

0.0
0.3
0.6
Ex
FS

0.0
0.1
0.2

0
2
4
Exc
Inh
Global OSI
A
B
C
D
*
*
** ***
ST1 ST2 DR ST1 ST2 DR
F
Global OSI
Peak G (nS)
ST1 ST2 DR ST1 ST2 DR
**
**
Exc Inh
DR
FS
Normal
development
Thalamic input
Dark
rearing
Ex
1ms
Vm Vm
OSI
0.0
0.1
Exc
Inh

***
ST1 ST2 DR ST1 ST2 DR
***
0.4
0.6
0.8
1.0
0 90 180
Norm. G
θ (º )
Exc
Inh
0.4
0.6
0.8
1.0
0 90 180
θ (º )
First cycle Cycle averaged
ST1 ST2 DR ST1 ST2 DR
0
20
40
Pref
Orth
E
FS
Normal
development
Thalamic input
Dark
rearing
Ex
**
**
Firing rate (Hz)
***
***
**
**
**
**
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5.3.5 Development of inhibitory neuron tuning
The broadening of inhibitory tuning can be attributed to two factors: 1) a broadening of tuning
of individual inhibitory neurons; 2) an increase in the convergence of inhibitory inputs with
diverse orientation preference on a common excitatory cell. A recent study in layer 2/3 of
mouse visual cortex has demonstrated that the orientation tuning of PV+ fast-spiking neurons
is broadened during a similar developmental window (Kuhlman et al., 2011). In layer 4, fast-
spiking neurons are a predominant source of inhibition, since they account for 50-70% of
inhibitory neurons in this layer (Kawaguchi and Kubota, 1997; Gonchar et al., 2007; Xu et al.,
2010). We thus tested whether changes of tuning of this population of inhibitory neurons could
contribute to the developmental broadening of inhibitory inputs to excitatory neurons. Fast-
spiking neurons in our loose-patch recordings were identified by their narrow spike waveforms
(see Methods). Consistent with the report in layer 2/3, we found that the tuning selectivity of
fast-spiking neurons in layer 4 was significantly reduced from ST1 to ST2 (Figure 5.4A, red).
Consistently, the tuning width of fast-spiking neuron responses was broadened (42.1 ± 8.5º at
Figure 5.4 Effects of dark rearing and tuning of inhibitory neurons. A, Average gOSI of spiking responses
of excitatory neurons (Ex; black) and FS inhibitory neurons (FS; red) at ST1 and ST2 of normal development and
under dark rearing (DR, tested at ST2). N25, 17, 21, 12, 15, 13 from left to right. Error bars indicate SEM.
*p<0.05; **p<0.05; ***p<0.001; ANOVA with post hoc test. Inset, Example spike waveforms of recorded FS
neurons. B, Example excitatory and inhibitory synaptic responses in a dark-reared animal at P25. Calibration: 100
pA (exc)/270 pA (inh), 200 ms. Bottom, Normalized synaptic tuning curves. C, Average gOSI (top) and OSI
(bottom, see Methods) for excitation (black) and inhibition (red) at ST1 and ST2 of normal development and
under dark rearing. n=13, 14, 11, respectively. Error bars indicate SEM. ***p<0.001, ANOVA with post hoc test.
D, Average peak conductance of cycle-averaged responses to optimal orientation. **p<0.01, ANOVA with post
hoc test. n=13, 14, 11. E, Average firing rate of FS neurons at the preferred and orthogonal orientations. Error
bars indicate SD. **p<0.01, ANOVA with post hoc test. n=12, 15, 13. F, Proposed circuit models. Top, A simple
feedforward circuit can explain the normal development of orientation selectivity. FS neurons and their target
layer 4 excitatory neuron receive a similar set of thalamic input, thus they exhibit similar preferred orientations.
Strengthening of thalamocortical drive onto FS neurons (as indicated by the thickening of arrows) elevates their
V m responses, resulting in a broadening of their spiking responses (spike threshold is indicated by a horizontal
dotted line). Bottom, Under dark rearing, the thalamocortical drive is not strengthened. The connections from
inhibitory neurons preferring other orientations (marked by a different shade, which then receive different sets of
thalamic input) are unselectively enhanced.
107

ST1 and 53.4 ± 16.3º at ST2, mean ± SD, p < 0.01, t-test). The evoked firing rate of fast-spiking
neurons at the preferred orientation was developmentally increased (9.2 ± 6.2 Hz at ST1 vs.
22.2 ± 11.2 Hz at ST2, p < 0.01), similar to excitatory neurons. But different from excitatory
neurons, firing rate of fast-spiking neurons at the orthogonal orientation was also increased
(4.2 ± 2.7 Hz at ST1 vs. 15.9 ± 8.4 Hz at ST2, p < 0.01) (Figure 5.4E). Together, these data
demonstrate that a weakening of tuning selectivity of individual inhibitory neurons does
contribute to the normal developmental broadening of inhibition.
We also examined tuning of fast-spiking neurons in dark reared animals. As shown in Figure
4A, the developmental weakening of orientation tuning of fast-spiking neurons was impaired
by dark rearing, and they remained as sharply tuned as they were at ST1 (tuning width was
41.3 ± 9.3º under dark rearing vs. 53.4 ± 16.3º at normal ST2, p < 0.01). In addition, firing rate
of fast-spiking neurons was largely reduced by dark rearing (Figure 5.4E). These data indicate
that the developmental reduction of fast-spiking neuron selectivity depends on visual
experience. A similar conclusion has been made for layer 2/3 fast-spiking neurons (Kuhlman
et al., 2011). Nevertheless, blocking the broadening of output responses of individual
inhibitory neurons does not block the broadening of the aggregate inhibitory input to excitatory
neurons, indicating that dark rearing may have induced an increase in the convergence of
inhibitory neuron inputs with diverse orientation preference. Consistent with this notion, we
observed that in dark reared animals the difference in preferred orientation between excitation
and inhibition (Δθpref) was noticeably increased (20.5 ± 7.9º for normal ST2 vs. 31.5 ± 28.7º
for dark rearing, mean ± SD).

108

Discussion
Although the developmental maturation of orientation selectivity has been characterized
thoroughly, understanding of the underlying synaptic mechanisms has lagged behind. To our
knowledge, this study is the first to reveal the detailed tuning properties of excitatory and
inhibitory synaptic inputs underlying orientation selectivity in the developing cortex. Different
from previously thought, the excitatory tuning is not significantly sharpened during a post eye-
opening period, while an inhibitory mechanism, i.e. broadening of the inhibitory tuning and
strengthening of inhibitory inputs primarily accounts for the developmental sharpening of
orientation selectivity. Our results however do not rule out the possibility that before the onset
of visual experience, spontaneous activity drives rearrangements of excitatory neuronal
connections that mediate an early phase of development of orientation tuning (Miller et al.,
1999).
5.4.1 An inhibitory mechanism underlying orientation selectivity
sharpening
Our results demonstrate that the strength of excitatory synaptic inputs evoked by optimally
oriented stimuli is developmentally increased, while the excitatory tuning selectivity remains
unchanged. This indicates that excitatory responses to different orientations are scaled up, or
increased proportionally during development. On a global scale, inhibition is up-regulated in
a balanced manner, since the ratio between the strengths of optimally evoked excitation and
inhibition is relatively constant. Such balanced increase of excitation and inhibition may allow
an increased evoked firing rate while preventing response saturations (Turrigiano and Nelson,
2004; Pouille et al., 2009). However, simply scaling up excitation and inhibition without
109

modifying their tuning profiles would result in reduced tuning selectivity, as shown by our
modelling and dynamic clamp results (Figure 5.3), which are also consistent with our previous
results in the adult cortical study (Liu et al., 2011). Increasing inhibitory input strength together
with broadening its tuning profile effectively sharpens orientation tuning by reducing
membrane potential responses to non-preferred stimuli. This result is reminiscent of previous
modelling studies of adult visual cortical circuits, which employed more broadly tuned
inhibitory interactions than excitatory interactions to generate sharp orientation selectivity in
the face of weakly tuned feedforward excitation (Somers et al., 1995; Lauritzen and Miller,
2003). Noticing that the tuning of excitation is considerably weak in mouse cortical neurons
with the response to the orthogonal orientation usually larger than half of that to the optimal
orientation (Figure 5.2E; also see (Jia et al., 2010; Liu et al., 2011)), the broadening of
inhibition is particularly important for achieving sharp orientation selectivity. Previously, we
have reported for layer 2/3 simple cells in the adult cortex that inhibition is more broadly tuned
than excitation (Liu et al., 2011). Our present results in layer 4 demonstrate that inhibition
becomes more broadly tuned than excitation during development, and that this broadening has
important functional significance.
The current results in the V1 appear somewhat different from those in the developing primary
auditory cortex (A1). In layer 4 of the rat A1, previously we reported that frequency selectivity
(as reflected by the half-maximum bandwidth of the frequency tuning curve) of excitation is
sharpened after the onset of hearing, while that of inhibition is not significantly changed ((Sun
et al., 2010), but see (Dorrn et al., 2010)). Due to the differential changes in excitatory and
inhibitory tuning selectivity, eventually in the adult A1 the selectivity of inhibition becomes
110

lower than that of excitation (Sun et al., 2010). The different synaptic mechanisms for the
refinement of frequency and orientation tuning, especially a lack of refinement of excitation in
the case of orientation tuning, may reflect a difference in neural circuitry underlying these
feature selectivities. In the visual system, orientation selectivity is generated in the cortex and
is heavily influenced by synaptic circuitry within the cortex. In the auditory system, frequency
selectivity is generated in the periphery and is relayed along the ascending pathways.
Sharpening of frequency tuning of neurons at subcortical stages can result in a sharpening of
excitatory inputs to cortical neurons. On the other hand, the effect on inhibitory tuning in the
A1 is determined by combined changes of tuning of subcortical neurons and that of
intracortical inputs. It is also worth noting that orientation tuning properties of synaptic inputs
in the mouse layer 4 appear different from other species such as cat. Inhibition is more broadly
tuned than excitation in the mouse layer 4 at more mature stages, whereas in the cat inhibition
and excitation have similar tuning widths (Anderson et al., 2000a; Marino et al., 2005).
Considering this difference and the fact that the organization of orientation selectivity in
rodents is different from other species (Ohki et al., 2005; Van Hooser et al., 2005; Kerlin et
al., 2010; Runyan et al., 2010), how general the developmental synaptic mechanisms revealed
in this study are will be an interesting and important issue for future investigations.
5.4.2 Circuit models for the development of orientation selectivity
Based on our results on synaptic inputs to excitatory neurons and spiking responses of fast-
spiking inhibitory neurons, we propose a simple feedforward circuit model that may explain
the sharpening of orientation tuning during normal development as well as in dark rearing
conditions (Figure 5.4F). In this model, fast-spiking neurons providing predominant inhibition
111

onto the layer 4 excitatory neuron receive a similar set of thalamic input as their target, so that
inhibition and excitation onto the excitatory neuron have similar preferred orientations. During
development, thalamic inputs are strengthened, increasing excitatory drive onto both the fast-
spiking neurons and the excitatory neuron. Possibly because fast-spiking neurons receive less
inhibitory control than excitatory neurons (Gabernet et al., 2005), the increased thalamic drive
onto fast-spiking neurons results in a large increase in their firing rate as well as a broadening
of their spiking response tuning attributable to a prominent “iceberg” effect (Somers et al.,
1995; Lauritzen and Miller, 2003) and a selectivity blurring effect (Figure 5.4F, upper panel,
(Liu et al., 2011)). The strengthened and broadened inhibition onto the excitatory neuron, on
the other hand, can counteract its increased excitatory drive and sharpen its output responses.
At the first look, a broadening of inhibitory tuning seems not required for the sharpening of
orientation selectivity of excitatory neurons, since dark rearing prevents the broadening of fast-
spiking neuron responses while the sharpening of excitatory neuron responses is not affected
in layer 4 (Figure 5.4A) or in layer 2/3 (Kuhlman et al., 2011). However, we find that the
aggregate inhibitory input still becomes broadened and strengthened in dark rearing conditions,
despite that individual fast-spiking inhibitory neurons remain as sharply tuned and weakly
responding. This indicates that in dark rearing conditions the simple feedforward model is not
valid any more, and that additional inhibitory sources must have been recruited. As previous
in vitro studies have shown that depriving visual input induces a marked strengthening of fast-
spiking neuron to excitatory neuron connections within layer 4 (Maffei et al., 2006), we
propose that dark rearing results in a non-selective enhancement of synaptic connections from
inhibitory neurons with very different orientation preferences than the excitatory neuron itself
112

(Figure 5.4E, bottom panel). This recruitment of other inhibitory neuron sources can lead to
the apparently normal strengthening and broadening of inhibition in dark rearing conditions.
Different from inhibition, dark rearing prevents the developmental strengthening of excitation,
which leads to the reduced firing rate of fast-spiking neurons. This may suggest that the
developmental strengthening of thalamocortical inputs is driven by visual input. More detailed
analysis is needed in the future to test these proposed models.

113

Chapter 6: Synaptic Circuits Underlying the Different
Orientation Selectivity between Simple and Complex Cells
Introduction
In the V1, there are mainly two type neurons based on the receptive field structure. Simple
cells have separated ON and OFF receptive fields, while complex cells show overlapped
receptive fields (Hubel and Wiesel, 1962; Heggelund, 1986). The V1 is the first site along the
visual pathway where neuronal responses exhibit robust sensitivity to orientation of stimuli
(Hubel and Wiesel, 1962). The orientation selectivity is likely important for tasks such as edge
detection and contour completion. Intense studies have showed that both simple and complex
cells respond preferentially to specific orientation (Hubel and Wiesel, 1962; Campbell et al.,
1968; De Valois et al., 1982; Niell and Stryker, 2008). Interestingly, simple cells are found
more sharply tuned than complex cells in cat and monkey studies (Henry et al., 1974; Rose
and Blakemore, 1974; Watkins and Berkley, 1974; Ikeda and Wright, 1975; Schiller et al.,
1976; De Valois et al., 1982).
Despite extensive studies in the past decades, how orientation selectivity is created by the
computation of neural circuits is still an issue under intense debate (Sompolinsky and Shapley,
1997; Ferster and Miller, 2000; Shapley et al., 2003). In particular, how the cortical inhibitory
process is involved in sculpting orientation tuning has remained controversial. In one view,
cortical inhibition does not contribute significantly to the creation of orientation selectivity
(Ferster et al., 1996; Anderson et al., 2000a). The orientation-tuned excitatory inputs,
114

attributable to a linear arrangement of receptive fields of relay cells (Chapman et al., 1991;
Reid and Alonso, 1995; Ferster et al., 1996), are thought to be sufficient to generate orientation
selectivity under a spike thresholding mechanism (Anderson et al., 2000a; Priebe and Ferster,
2008). In a contrasting view, inhibition is required to sharpen orientation selectivity (Sillito,
1975; Tsumoto et al., 1979; Sillito et al., 1980; Ringach et al., 2003). Except for a few cases
(Wu et al., 2008; Poo and Isaacson, 2009), a match of excitatory and inhibitory tunings is
widely observed in the sensory cortex (in cat visual cortex, (Anderson et al., 2000a; Monier et
al., 2003; Marino et al., 2005; Priebe and Ferster, 2005); in rodent auditory and somatosensory
cortex, (Wehr and Zador, 2003; Zhang et al., 2003; Tan et al., 2004; Okun and Lampl, 2008;
Tan and Wehr, 2009)).
While previous mechanistic studies were mostly carried out in cats, mouse visual cortex has
recently emerged as an important experimental model for visual research. Recent recordings in
the mouse V1 have shown that similarly as in the cat V1, spiking responses of neurons can be
strongly orientation-tuned (Mangini and Pearlman, 1980; Niell and Stryker, 2008; Liu et al.,
2009). However, the spatial distribution of excitatory and inhibitory synaptic inputs largely
differs from that proposed for cat simple cells (Liu et al., 2010), implying that the mouse circuits
for orientation selectivity might be different from those in cats. First, each synaptic subfield (ON
or OFF, excitatory or inhibitory) often possesses a rather round shape with small aspect ratios,
which suggests that the spatial arrangement of synaptic inputs may not sufficiently account for
orientation selectivity. Second, while excitation and inhibition are organized in a spatially
opponent manner in cat simple cells (Ferster, 1988; Hirsch et al., 1998; Anderson et al., 2000a),
in mouse both simple cells and complex cells the excitatory and inhibitory subfields for the same
115

contrast display a large spatial overlap, suggesting that excitation and inhibition evoked by
oriented stimuli may temporally overlap significantly at whichever stimulus orientation. These
properties of synaptic inputs to mouse neurons suggest that inhibition can play a significant role
in determining the different orientation tuning properties of the spike responses between simple
cells and complex cells.
Albeit extracellular recordings have been intensively carried out, the synaptic mechanism
underlying the different orientation tuning between simple and complex cells are largely
unknown. In the present study, by combining in vivo cell-attached recording, whole-cell
current-clamp recording and whole-cell voltage-clamp recording, we dissected the synaptic
circuitry contributing the difference of orientation tuning between simple cells and complex
cells in layer 2/3 of mouse V1. Our data showed that excitatory and inhibitory inputs largely
overlapped both temporally and spatially. Simple cells and complex cells received similarly
broadly tuned excitatory inputs. Inhibition shared the similar preferred orientation as
excitation. Interesting, comparing the tuning of excitation, the tuning of inhibition was
significantly broader in simple cells and significantly narrower in complex cells. We also found
that orientation tuning strength of synaptic inputs is related to the aspect ratio synaptic
receptive fields in both cell types. The results indicated that the cortical inhibition may play a
vital role in shaping the tuning properties in different cell types.
Methods
6.2.1 Animal preparation
Animal was prepared as described in 3.2.1.
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6.2.2 In vivo electrophysiology
The blind recording was performed as described in 2.2.3 and 3.2.2. All neurons recorded in
this study were located at a depth of 220-350µ m below the pia according to the microdrive
reading, corresponding to layer 2/3. According to previous results, the majority of layer 2/3
excitatory cells are simple and complex cells based on spiking receptive field structure (Gentet
et al., 2000).
6.2.3 Visual stimulation
The visual stimulation was set up as described in 2.2.5. The drifting sinusoidal gratings and
drifting bars as described in 2.2.5 were applied to measure the orientation selectivity. Flash
light and dark squares (5   5) or flash light and dark bars (4   60) at preferred orientation
were applied to map the receptive field.
6.2.4 Data analysis
The data acquisition and analysis of spike responses is the same as described in 2.2.7. In
current-clamp recordings, subthreshold Vm responses were analyzed after removing spikes
with an 8 ms median filter (Carandini and Ferster, 2000). Complex cells were identified by
overlap index (OI) of spike responses >0.3 or OI of membrane potential responses > 0.71
according to previous criteria (Gentet et al., 2000; Liu et al., 2010).
In voltage-clamp recordings, excitatory and inhibitory conduces were derived as described in
3.2.4. The strength of orientation selectivity were quantified as in 2.2.7 and 5.2.4.

117

Results
6.3.1 Simple cells showed stronger orientation selectivity than complex cell
for spike responses in mouse V1
Previous studies have showed that simple cells are more orientation selective than complex
cells in cat and monkey V1 (De Valois et al., 1982). In this study, by performing blind loose-
patch clamp recording, we first examined the difference of orientation selectivity between
simple and complex cells in layer 2/3 of mouse V1. The receptive fields were mapped to
determine the cell types, as well as the relation between receptive field structure and orientation
selectivity. As shown in Figure 6.1A, the example simple cell displayed a receptive field with
spatially segregated ON and OFF subfields. When tested with drifting sinusoidal gratings, the
cell responded maximally to vertically oriented gratings (Figure 6.1B). As shown in Figure
6.1C, the example complex cell displayed a receptive field with largely overlapped ON and
OFF subfields. The cell responded maximally when the sinusoidal gratings drifted at 150º
orientation angle (Figure 6.1D). To classify the cell types, we quantified the spatial
relationship between ON and OFF subfields based on OI, and compared with the classification
based on F1/F0 ratio (Figure 6.1E). We set OI 0.3 as a boundary between simple and complex
cells (Gentet et al., 2000). In a summary of 72 cells, 38 cells is classified as complex cells, and
only 16 complex cells showed F1/F0 ratio smaller than 1. Although there is a correlation
between OI and F1/F0 ratio, the classification based on the two criteria is not consistent (Van
Hooser et al., 2013). Actually, our data indicated that more than half complex cells are
classified as simple cells if only based on F1/F0 ratio, which overestimated the ratio of
complex cells in layer 2/3 (Niell and Stryker, 2008). By examining the relationship between
118

0.0 0.5 1.0
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F1/F0
-90 0 90 180
-90
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90
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Simple Cell
Complex Cell

RF major axis ( °)
Preferred θ ( °)
OI
OSI
ON OFF
F
60 120 180 240
0
10
20
Orientation angle θ ( °)
Spike #
18Hz
0
G
C D
E
***
A B
ON OFF
8.3 Hz
0
60 120 180
0
20
40
Orientation angle θ ( °)
Spike #
Figure 6.1 Simple cells showed stronger orientation selectivity than complex cell for spike responses in
layer 2/3 of mouse V1. A, Spike receptive fields of an example simple cell measured by loose-patch clamp
recording. Top, arrays of post-stimulus spike-time histograms (PSTHs, generated from all the trials) for spike
responses to unit ON and OFF stimuli. PSTHs were arranged according to the corresponding stimulus locations.
Each pixel represents visual space of 5º . Red and green ovals depict the two-dimensional Gaussian fits of the ON
and OFF subfields, respectively. Scale: 20Hz, 200ms. Bottom, color maps for spike ON (red) and OFF (green)
responses. The brightness of color represents the average evoked firing rate. The maps were smoothed by bilinear
interpolation. The white line depicts the orientation of receptive field major axis. B, Orientation tuning of the
same cell in (A). Left, PSTHs for spike responses evoked by drifting sinusoidal gratings at 12 orientations. Arrow
indicates the drifting direction of the grating. Scale: 30Hz, 200ms. Right, orientation tuning curve as measured
by the evoked spike numbers. The responses to gratings of opposite directions were averaged for each orientation.
Red dash curve indicates the Gaussian fit. Error bar = SEM. C, An example complex cells presented similar as in
A. Scale: 20Hz, 200ms. D, Orientation tuning of the same cell in (C) presented the same as in B. Scale: 16Hz,
300ms. E, Plot of modulation ratio (F1/F0) against overlap index (OI). The blue line is the linear fitting. r = -0.72.
The dashed line marks OI=0.3 (which separates simple from complex cells) and F1/F0=1, respectively. F, Plot of
orientation selectivity index (OSI) against OI. The blue line is the linear fitting. r = -0.70. Inset, Comparison of
OSI between simple (n=25) and complex (n=39) cells. ***, p<0.001, t-test. G, The relationship between preferred
orientation angle and receptive field major axis. The red line is the identity line. Noting only cells with OSI >0.3
are plotted.
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OSI and OI, we found that OSI was negatively correlated with OI, and complex cells have
significantly weak orientation selectivity than simple cells (Figure 6.1F). For cells with
OSI>0.3, we examined the relationship between receptive field major axis (see Methods) and
preferred orientation for both simple cells and complex cells. As shown in the example cells,
as well as a summary of 28 orientation selective complex cells and 43 orientation selective
simple cells, the preferred orientation is quite consistent with the receptive field major axis.
According to this result, we applied 1-D flashing bar at preferred orientation to map the
receptive field and determine the cell type in intracellular recordings.
6.3.2 Simple cells showed stronger orientation selectivity than complex cells
for membrane potential responses
To investigate the different orientation selectivity of simple cell and complex cells from
subthreshold responses, we carried out in vivo whole-cell current-clamp recording with K
+
-
gluconate based internal solution. As shown in the example simple cell (Figure 6.2A, 6.2B),
robust membrane depolarization responses were evoked by gratings at all testing orientations,
although significant spiking responses were only observed for two orientations. Therefore, the
orientation tuning of postsynaptic potential (PSP) response was much weaker compared to that
of spiking response, although the two types of response exhibited the same optimal orientation
(Figure 6.2B). Similar results could be observed from the example complex cell (Figure 6.2C,
6.2D). Actually, for orientation selective cells, the preferred orientation is essentially the same
for spike and membrane potential responses (Figure 6.2E). For all recorded cells, the OSI of
spike responses is positively correlated with that of membrane potential responses. The
selectivity of spiking response was much stronger than that of PSP response (Figure 6.2F),
120

consistent with many intracellular recording results showing that spike thresholding can be a
powerful mechanism for sharpening response selectivity (Anderson et al., 2000a; Carandini
and Ferster, 2000; Schummers et al., 2002; Van Hooser et al., 2006; Priebe and Ferster, 2008;
Jia et al., 2010; Liu et al., 2010).The selectivity is much weaker measured from membrane
potential responses. If OSI of 0.3 from spike responses is set as a criterion for orientation
C D
0 90 180
0
5
10
15
θ ( °)
PSP (mV)
0 90 180
0
10
20
30
Spike #
F
0.0 0.1 0.2 0.3
0.0
0.5
1.0
OSI
Vm
OSI
AP
E
0 90 180
0
90
180
Simple cell
Complex Cell
θ
Vm
( °)
θ
AP
( °)
0.0
0.1
0.2
**
OSI
Vm
S C
G H
-60
-40
-20
0
20
(mV)
Vr Vthr
n.s.
n.s.
S C
θ ( °)
-60 0 60
0
10
20

Spike #
-60 0 60
0
10
20
30
PSP (mV)
A B
AP Vm AP Vm
Figure 6.2 Simple cells showed stronger orientation selectivity than complex cell for membrane potential
responses. A, Spike (AP, left) and subthreshold membrane potential (Vm, with spikes filtered out, left) responses
of another simple cell examined by whole-cell current-clamp recording. Scale, 40Hz (spike)/ 21mV (Vm), 200ms.
B, The orientation tuning curves of spike (top) and Vm (bottom) responses for the cell in (A) and the
corresponding Gaussian fits (red dash curves). Vm response was measured as the peak depolarization level
relative to the resting membrane potential in the cycle-averaged waveform (first 3 cycles). Error bar = SEM. C,
Spike and subthreshold membrane potential responses of another complex cell. Scale, 200ms, 40Hz (spike)/
21mV (Vm). D, The orientation tuning curves of spike (top) and Vm (bottom) responses for the cell in (C)
presented as the same as in B. E, The plot of preferred orientation angle for spike responses versus that for Vm
responses for simple cells and complex cells. The red dash line is the identity line. F, The plot of OSI for spike
responses versus that for Vm responses for simple and complex cells. The red dash line is the identity line, and
blue line is the linear fitting. r = 0.81. The horizontal black line marks 0.3 and vertical black line marks 0.06. G,
Comparison of OSI from Vm between simple and complex cells. **, p<0.01, t-test. H, Comparison of spike
threshold and resting membrane between simple and cells. n.s., no significant difference, p=0.42 and 0.25
respectively.

121

selective neuron, the corresponding OSI from membrane potential responses is 0.06, which is
set as a criterion for the following intracellular recording. We classified cell types based on
their spike receptive fields, and measured OSI from subthreshold responses. We found that
simple cells are more orientation selective than complex cells for membrane potential
responses (Figure 6.2G). Previous studies showed spike threshold played an important role in
sharpening orientation tuning (Carandini and Ferster, 2000; Priebe and Ferster, 2008), thus we
examined the spike threshold and resting membrane potential. Our data showed no significant
differences between simple cells and complex cells for both spike threshold and resting
membrane potential (Figure 6.2H), indicating spike threshold does not contribute to the
different orientation tuning of simple cells and complex cells.
6.3.3 Orientation tuning of excitatory and inhibitory synaptic inputs to
simple cells and complex cells
To understand how the orientation tuning of membrane potential responses arises from the
integration of synaptic inputs, we applied whole-cell voltage-clamp recording with Cs
+
-
gluconate based internal solution to isolate excitatory and inhibitory inputs evoked by oriented
stimuli (see Methods). We used a cesium-based intracellular solution containing QX-314, which
blocked spike generation. Recordings with good voltage-clamp quality were achieved under our
experimental condition, as evidenced by the linear current-voltage relationship and the proximity
of the derived reversal potential of early synaptic currents to 0 mV (Liu et al., 2010). Under
current-clamp mode, we first recorded membrane potential responses to drifting bars of various
orientations to determine the preferred orientation and the orientation selectivity of the cell (data
not shown). Note that these PSP responses represented bona fide membrane potential responses
122

which had not been disturbed by spike generation. Because of the strong correlation between
OSI of spike response and OSI of Vm, only cells with OSI>0.06 for Vm were considered as
orientation selective cells and chosen for further analysis (Figure 6.2F). Because of the strong
correlation between the preferred orientation and the major axis of receptive field, we could use
flashing bright/dark bars of preferred orientation to map the one-dimensional receptive field to
determine the cell type. Neurons with an OI≥0.71 were classified as simple cell and neurons with
OI<0.71 were classified as complex cells (Liu et al., 2010).
As shown by the example simple cell, the PSP responses to bright (ON) and dark (OFF) bars
were substantially overlapping in space (Figure 6.3A). However, the maximum ON and OFF
responses were clearly segregated. Based on the average spike threshold of mouse V1 neurons
(22.4 ± 6.3 mV above the resting potential, mean ± SD, n =19 cells), the recorded PSP responses
would result in spatially distinct spiking ON and OFF subfields, indicating that the cell was most
likely a simple cell (Figure 6.3A). The overlapping ON and OFF subthreshold subfields with
segregated maximum ON and OFF responses were also observed for simple cells in our previous
study of two-dimensional synaptic receptive fields (Liu et al., 2010).
Under voltage-clamp mode, we next recorded the excitatory and inhibitory synaptic currents
evoked by drifting bars of various orientations, with the cell’s membrane potential clamped at -
70 and 0 mV, respectively. Robust excitatory and inhibitory responses were observed at all
testing orientations (Figure 6.3B). Notably, the amplitude of the excitatory responses varied in
an orientation-dependent manner, while this was less obvious for the inhibitory responses. From
the tuning curves plotted for the peak amplitude of synaptic conductances, it became clear that
the inhibitory input exhibited weaker orientation tuning than the excitatory input (Figure 6.3C).
123

ON
OFF
D E
F
Exc Inh
0 90 180
0
1
2
0.0
1.5
3.0

Exc

Inh
Ge (nS)
Gi (nS)
θ ( °)
B
C
A
-60 0 60
0
1
2

Exc
0
2
4
6
Inh

Ge (nS)
Gi (nS)
θ ( °)
Exc Inh

20 40
0
20
40

V
thr
Spatial location ( °)
△Vm (mV)
ON
OFF
ON
OFF
Figure 6.3 Orientation tuning of excitatory and inhibitory synaptic inputs to example simple cells and
complex cells. A, One-dimensional receptive field of Vm responses to flashing bars of preferred orientation for
an example simple cell. Top, average Vm responses evoked by bright (ON) and dark (OFF) bars at different
spatial locations. Bar width was 3.5° . The solid and dash curves depict the spatial tuning curves (i.e. the envelope
of peak response amplitudes) for ON and OFF responses, respectively. Scale, 500ms, 18mV. Bottom,
superimposed ON and OFF spatial tuning curves. An arbitrary spike threshold (V thr) of 22 mV above the resting
level was applied. B, Average excitatory (Exc) and inhibitory (Inh) currents evoked by bars of various orientations
for the same cell in A. Scale, 300ms, 70pA (Exc)/ 171pA (Inh). C, Orientation tuning curves for excitatory (red)
and inhibitory (blue) conductances for the same cell in B. D, One-dimensional receptive field of Vm responses to
flashing bars of preferred orientation for an example complex cell, presented the same as in A. Scale, 500ms,
8mV. E, Average excitatory and inhibitory currents evoked by bars of various orientations for the same cell in D.
Scale, 300ms, 150pA (Exc)/ 230pA (Inh). F, Orientation tuning curves for excitatory (red) and inhibitory (blue)
conductances for the same cell in E.
124

We obtained similar results from a total of twelve simple cells, identified by the relative
separation of maximum ON and OFF PSP responses.
As shown by the example complex cell, the ON and OFF subfields were largely overlapped
and the maximum ON and OFF responses were also overlapped (Figure 6.3D, OI=0.9). And
with an OSI of 0.19 measured by membrane potential responses (data not shown), it is
classified as an orientation selective neuron. As shown in the same cell, while gratings at all
direction could evoke robust synaptic responses, the inhibition showed narrower orientation
tuning than excitation (Figure 6.3E, 6.3F). The similar observation could be made from a total
of fourteen complex cells.
6.3.4 Inhibition mediated different orientation selectivity in simple cells and
complex cells
As shown by the averaged tuning curves, inhibition showed broader tuning than excitation in
simple cells and narrower tuning in complex cells (Figure 6.4A, 6.4B). While the excitation
showed similar tuning between simple cells and complex cells, the inhibition in simple cells
showed much broader tuning than that in complex cells. In a summary of 12 orientation
selective simple cells, the averaged OSI for excitation and inhibition are 0.26± 0.09 and
0.12± 0.10 respectively (Figure 6.4C, paired t-test, p<0.001). Conversely, in a summary of 14
orientation selective complex cells, the averaged OSI for excitation and inhibition are
0.20± 0.11 and 0.31± 0.14 respectively (Figure 6.4C, paired t-test, p<0.001). Consistent with
the averaged tuning curves, the excitatory tuning of complex cells is not significance than that
of simple cells (Figure 6.4C, p=0.07), while the inhibition tuning is significant narrower
(Figure 6.4C, p<0.001). Thus our data indicates cortical inhibition play a vital role in shaping
125

orientation tuning in visual cortex. We also found that excitation and inhibition prefer the
similar orientation as membrane potential responses in both simple cells and complex cells
(data not shown).
To further investigate the relative contributions of excitation and inhibition, we analyzed the
E/I ratio at the preferred and orthogonal orientation. Consistent with their synaptic tuning
profiles, complex cells and simple cells showed significantly lower and higher E/I ratio
receptively at preferred orientation than that at orthogonal orientation (Figure 6.4D, t-test,
Figure 6.4 Orientation tuning of excitatory and inhibitory synaptic inputs to simple cells and complex cells.
A, Average tuning curves (normalized) for excitatory input (Exc) and inhibitory input (Inh) of simple cells. N =
12. The tuning curves were aligned according to the preferred orientation angle, which is set as 0° . Error bar =
SEM. B, Average tuning curves (normalized) for excitatory input (Exc) and inhibitory input (Inh) of complex
cells. N = 14. C, Plot of OSI of inhibitory input versus that of excitatory input for 12 simple cells and 14 complex
cells. Blue symbol = mean ± SD. The red dash line is the identity line. OSI of inhibition is significantly lower
than that of excitation for simple cells (p < 0.001, paired t-test), and significantly higher than that of excitation
for complex cells (p < 0.001, paired t-test). D, E/I ratio at preferred and orthogonal orientation for simple and
complex cells. ***, p<0.001, paired t-test.
C
0.00 0.25 0.50
0.00
0.25
0.50
Simple cell
Complex cell
OSI
Exc
OSI
Inh
D
0 90 180
0.0
0.5
1.0
Exc
Inh
Norm. Amp
θ ( °)
A B
0.0
0.5
1.0
E/I
Pref Orth Pref Orth
***
***
***
Simple cell Complex cell
0 90 180
0.0
0.5
1.0
Exc
Inh
Norm. Amp
θ ( °)
Simple cell Complex cell
126

p<0.001). Remarkably the E/I ratio at orthogonal orientation is significantly higher in complex
cells than that in simple cells (Figure 6.4D, t-test, p<0.001), indicating the weaker tuning of
complex cells of spike responses is mainly contributed by different excitatory and inhibitory
inputs at orthogonal orientation rather than that at preferred orientation.
6.3.5 Membrane filtering and inhibitory sharpening of blurred selectivity
As shown in Figure 6.5A, the intrinsic input-output transformation could lead to a blurring of
tuning selectivity. To further illustrate this effect of membrane filtering, we carried out a more
generalized simulation using the neuron model. For simplicity, we simulated PSP responses
resulting from model excitatory inputs that vary only in amplitude but not in temporal profile.
The filtering property of the membrane is demonstrated in the plot of membrane potential
depolarization versus excitatory conductance (Figure 6.5A). Within a physiological range of
excitatory conductances, the input-output function exhibited a fast saturating curve (Figure
6.5A). Its first-order derivative decreased rapidly to a small value (Figure 6.5A, inset),
indicating that within a large input range the increase of the PSP response was much slower
than the growth of the excitatory input strength. This is vividly demonstrated by two model
excitatory inputs with one twice as strong as the other (Ge1:Ge2 = 1: 2), which generated PSP
responses that had a much smaller fold difference in amplitude ( Vm1: ΔVm2 = 1: 1.2)
(Figure 6.5B). Such a ‘‘compression’’ effect has a great impact on stimulus selectivity of
neuronal responses. Imagine that Ge2 and Ge1 represent the excitatory inputs evoked by the
optimal and null stimuli, respectively. The selectivity existing in the excitatory inputs, as
reflected by the ratio of Ge2 to Ge1, is greatly attenuated when the inputs are transformed into
PSP responses. Since Ge can represent an input evoked by any type of physical stimulus, such
127

attenuation of tuning selectivity poses a ubiquitous problem for any feature-specific neuronal
responses.
To further illustrate the inhibitory effect on orientation selectivity, we modeled excitatory and
inhibitory inputs with their tuning profiles taken from experimental data, and simulated PSP
responses resulting from excitatory inputs alone and from integrating excitatory and inhibitory
inputs (Figure 6.5C). The PSP tuning was largely flattened when only excitatory inputs were
present (Figure 6.5D).
0.00 0.25 0.50
0.0
0.1
0.2
OSI
Inh
OSI
Vm
C
-90 0 90
0.0
0.5
1.0
θ ( °)
Norm. Gi
θ ( °)
Norm. PSP
-90 0 90
0.0
0.5
1.0
D E
Ge (nS)
0 5 10
0
30
60
Ge (nS)
PSP ’ (mV/nS)
PSP (mV)
0 5 10
0
50
100

Ge
1
Ge
2
△Vm
1
△Vm
2
A
0 1
0
1
2

Time (s)
Ge (nS)
0 1
0
20
40

Time (s)
△Vm (mV)
Ge
1
Ge
2
△Vm
1
△Vm
2
B
Figure 6.5 Membrane filtering and inhibitory sharpening of blurred selectivity. A, Plot of peak amplitude
of simulated PSP response versus that of excitatory conductance (Ge) without inhibition. Ge 1 and Ge 2 are the
amplitudes of two example excitatory inputs. ΔVm 1 and ΔVm 2 are the amplitudes of corresponding PSP responses
they generated. Inset, the first-order derivative of PSP-Ge function without inhibition. B, Temporal profiles of
two model excitatory inputs (1 nS and 2 nS peak conductance) (left) and their corresponding PSP responses
(right). C, The excitatory tuning (green curve) is fixed while varying the inhibitory tuning. Blue, untuned
inhibition; red, broader inhibition; green, co-tuned inhibition, black, narrower inhibition. D, The corresponding
PSP tuning curves. Dotted curve marks the PSP tuning in the absence of inhibition. E, OSI of PSP responses
versus that of inhibitory inputs, with the excitatory tuning fixed.
128

While exquisitely balanced inhibition can already achieve a sharpening of PSP tuning,
Inhibition being more broadly tuned than excitation is more advantageous since it can further
suppress the PSP response at orthogonal orientation (Figure 6.5D). We simulated orientation
tuning of PSP responses with a fixed excitatory tuning while varying the tuning strength of
inhibition. As shown in Figure 6.5E, as the tuning strength of inhibition is reduced, the
sharpening effect on the PSP tuning is enhanced.
Discussion
In this study, we investigated the orientation selectivity of simple cells and complex cells at
three different levels: spike output, membrane potential output and synaptic inputs. We found
that simple cells were more orientation selective than complex cells at spike responses and
membrane potential responses, and spike threshold did not contribute to the difference of
orientation tuning for spike responses between simple cells and complex cells. By dissecting
the synaptic inputs with in vivo voltage-clamp technique, we found inhibition shares the same
preferred orientation as excitation in both simple cells and complex cells. Interesting, inhibition
was more broadly tuned than excitation in simple cells, and more narrowly tuned in complex
cells. The orientation selectivity of excitation was similar between simple cells and complex
cells, while inhibition was more broadly tuned in simple cells. Thus our results indicated it is
inhibition that mediates the orientation selectivity in different cell types. By closely interacting
with excitation, inhibition ameliorates the membrane blurring of excitatory selectivity, or in
another word sharpens the blurred selectivity.

129

6.4.1 Classification of simple and complex cells
In cat and monkey studies, besides receptive field structure, modulation ratio to sinusoidal
gratings is another criterion to classify cell types (Skottun et al., 1991; Carandini et al., 1997;
Anderson et al., 2000a). Nonetheless, previous study in tree shrew V1 found that many cells
with high modulation ratio has largely overlapped ON and OFF subregions (Van Hooser et al.,
2013). Our data in mouse V1 also showed that 22 of 38 complex cells had a modulation ratio
value larger than 1.0, thus the ratio of complex cells is underestimated by this criterion (Niell
and Stryker, 2008).
6.4.2 Potential mechanisms underlying different orientation tuning of
inhibition in simple cell and complex cell
The synaptic inhibition which mediates the different orientation tuning of simple cell and
complex cell, may be provided by different subtype of inhibitory neurons. The broad inhibition
of simple cell may be provided by broadly tuned parvalbumin positive (PV+) inhibitory
neurons (Liu et al., 2009; Kerlin et al., 2010; Runyan et al., 2010), or by converged inputs from
tuned subtypes of PV+ neurons or tuned non-PV+ inhibitory neurons (Ma et al., 2010; Runyan
et al., 2010; Runyan and Sur, 2013). The sharp inhibition of complex cell can only be provided
by tuned inhibitory neurons. Potential candidates includes somatostatin positive (SOM+)
inhibitory neurons, which show strong orientation selectivity as excitatory neurons (Ma et al.,
2010), and other inhibitory neurons abundant in layer 2/3, such as Vasointenstinal peptide
positive (VIP+), calretinin positive (CR+), and neuropeptide tyrosine positive (NPY+) neurons
(Gonchar et al., 2007; Xu et al., 2010).
130

The synaptic orientation tuning differences between simple and complex cells can be largely
attributed to the E/I ratio at orthogonal orientation (Figure 6.4D). Because the orientation
tunings of excitation between simple cells and complex cells are similar, our results indicate
simple cells receives more inhibition at the orthogonal orientation rather than less inhibition at
the preferred orientation. One possibility is that complex cells receives less inputs from broadly
tuned PV+ inhibitory neurons and more inputs from tuned inhibitory neurons with similar
preferred orientation than simple cells.
6.4.3 Input-output function and different inhibitory mechanisms
The inhibitory effect observed in this study is different from a commonly proposed
normalization model, which was often used to explain the ‘‘iceberg’’ effect. The model is
based on a matching of tuning selectivity between excitation and inhibition, which has been
observed widely in sensory cortices (Monier et al., 2003; Wehr and Zador, 2003; Zhang et al.,
2003; Tan et al., 2004; Marino et al., 2005; Okun and Lampl, 2008; Tan and Wehr, 2009). It
proposes that inhibition scales down the membrane potential tuning by reducing responses in
a divisive manner (Carandini and Heeger, 1994; Murphy and Miller, 2003; Wehr and Zador,
2003; Katzner et al., 2011). Such operation does not alter the tuning shape or selectivity of
membrane potential responses per se. Considering that OSI is expressed by (Rpref – Rorth) /
(Rpref + Rorth), with Rpref and Rorth representing the responses at preferred and orthogonal
orientations, respectively, if Rpref and Rorth are divided by the same factor, OSI will remain the
same. The normalization model does increase the sharpness of spiking responses by elevating
the effective spike threshold. In this study, however, we do observe that inhibition causes a
change in tuning shape and an increase in OSI. This is due to an increase of (R pref – Rorth) and
131

a concomitant decrease of (Rpref + Rorth), together leading to a more effective enhancement of
tuning selectivity.
6.4.4 Implications on cortical circuits
In Hubel and Wiesel’s hierarchical model, orientation selectivity arises from the spatial
alignment of receptive fields of dLGN neurons to simple cells, and complex cells inherit
orientation selectivity from the convergence of simple cells (Hubel and Wiesel, 1962).
Extensive studies have been carried out to test both assumptions (Ferster et al., 1996; Alonso
and Martinez, 1998; Lampl et al., 2001; Levy and Dubois, 2006). However, this model is still
in debate. First, the basic assumption that the dLGN neurons are not orientation selective are
correct. Previous studies demonstrated the dLGN neurons do show weak orientation selectivity
(Vidyasagar and Urbas, 1982; Shou and Leventhal, 1989; Xu et al., 2002; Johnson et al., 2008;
Piscopo et al., 2013; Scholl et al., 2013; Zhao et al., 2013). Thus while the dLGN neurons
could provide orientation bias to cortical neurons (Ferster et al., 1996; Chung and Ferster,
1998; Li et al., 2013; Lien and Scanziani, 2013), the spatial alignment of receptive fields of
dLGN neurons is not necessary for generating this bias. Second, our data showed that
excitatory inputs received by simple and complex cells are non-distinguishable, indicating they
may be driven by the similar excitatory sources (Alonso and Martinez, 1998).

132

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Abstract (if available)

Abstract One key question in system neuroscience is how the brain function is achieved by the dynamic functional connection among different neurons. A neuron receives inputs from the synapses of its connected neurons, and fires action potentials as its output. In the visual cortex, the functional properties of a single neuron is defined by its action potentials, which are determined by the integration of different synaptic inputs. However, what’s the roles of different synaptic inputs for visual processing is poorly understood. Using mouse primary visual cortex (V1) as a model, I dissected the functional synaptic circuits by applying in vivo whole‐cell patch clamp technique and optogenetic tools. ❧ In my first project, we probed the relative contributions of thalamic excitation and cortical excitation for orientation selectivity, direction selectivity and spatial receptive field in layer 4. We silenced intracortical excitatory circuits with optogenetic activation of parvalbumin‐positive (PV+) inhibitory neurons, and compared visually evoked thalamic excitation with total excitation in the same layer 4 excitatory neurons. We found that thalamic excitation is direction and orientation selective, and shows slightly elongated spatial receptive field. Cortical excitation preserves the orientation and direction tuning of thalamic excitation, with a linear amplification of thalamocortical inputs of about threefold, and expands thalamic spatial receptive field in an approximately proportional manner. Thus, intracortical excitatory circuits faithfully reinforce the representation of thalamocortical information and influence the size of the spatial receptive field by recruiting additional cortical inputs. ❧ In my second project, we investigated the roles of synaptic inhibition for orientation selectivity with different stimulus contrast in layer 4. We found orientation selectivity of spike response is sharpened as contrast increased. The sharpening is caused by the scaling up of excitation and broadening of inhibition. Modeling revealed that the broadening of inhibition is critical for sharpening orientation selectivity from low to high contrast. Finally, broadening of inhibition can be attributed to a contrast‐dependent broadening of spike‐response tuning of PV+ neurons. Together the results indicate that modulation of synaptic inhibition sharpens orientation selectivity during changes of stimulus strength. ❧ In my third project, we explored the roles of synaptic inhibition for the development of orientation selectivity in layer 4. We found orientation selectivity of spike response is progressively sharpened during development. Synaptic excitation and inhibition strengthened in a parallel way. Orientation tuning of excitatory inputs keeps relatively unchanged, whereas the tuning of inhibitory inputs is broadened, and becomes significantly broader than that of excitatory inputs. Neuron modeling and dynamic‐clamp recording demonstrated that this developmental broadening of the inhibitory tuning is sufficient for sharpening orientation selectivity. Depriving visual experience by dark rearing impedes the normal developmental strengthening of excitation, but a similar broadening of inhibitory tuning, likely caused by a nonselective strengthening of inhibitory connections, results in the apparently normal orientation selectivity sharpening in excitatory neurons. Our results thus provide the first demonstration that an inhibitory synaptic mechanism can primarily mediate the functional refinement of cortical neurons. ❧ In my fourth project, we studied the synaptic circuitry underlying direction selectivity in layer 4. We found the simple cells receives direction‐tuned excitatory input but barely tuned inhibitory input. Excitation and inhibition exhibits differential temporal offsets under movements of opposite directions: excitation peaks earlier than inhibition at the preferred direction, and vice versa at the null direction. This can be attributed to a small spatial mismatch between overlapping excitatory and inhibitory spatial receptive field: the excitatory spatial receptive field was skewed and the skewness was strongly correlated with the direction selectivity, whereas the inhibitory receptive field was relatively spatially symmetric. Neural modeling revealed that the relatively stronger inhibition under null directional movements, as well as the specific spatiotemporal offsets between excitation and inhibition, allows inhibition to significantly enhance direction selectivity of output responses by suppressing the null response more effectively than the preferred response. Our data demonstrate that while tuned excitatory input provides a basis for direction selectivity, largely untuned and spatiotemporally offset inhibition contributes significantly to sharpening the selectivity in mouse visual cortex. Furthermore, amplitude‐dependent suppression can sufficiently result in the direction selectivity of excitation. ❧ In my fifth project, we turned to layer 2/3 and examined the synaptic mechanisms underlying orientation selectivity in simple and complex cells. We found simple cells are more orientation selective than complex cells for both spike responses and membrane potential responses. Inhibition is more broadly tuned than excitation in simple cell, and vice versa in complex cell. Compared with complex cell, the excitatory tuning is similar and inhibitory tuning is broader in simple cell. Thus our data showed that it is the synaptic inhibition rather than excitation that shapes the orientation tuning in different types of neurons. ❧ Taken together, in layer 4, cortical excitation faithfully reinforces the representation of feedforward information, and synaptic inhibition plays a vital role in maintaining the sharpness of feature selectivity. In layer 2/3, synaptic inhibition generates diverse selectivity in different types of neuron.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Li, Yatang (author)

Core Title Functional synaptic circuits in primary visual cortex

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Physiology and Biophysics

Publication Date 04/28/2014

Defense Date 03/13/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag contrast‐dependent orientation selectivity,direction selectivity,intracortical circuits,in‐vivo dynamic clmap recording,in‐vivo two‐photon guided loose patch clamp recording,in‐vivo whole‐cell current‐clamp recording,in‐vivo whole‐cell voltage‐clamp recording,neural modeling,OAI-PMH Harvest,optogenetics,orientation selectivity,receptive field,synaptic excitation and inhibition,thalamocortical circuits,visual development

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Zhang, Li I. (committee chair), Chen, Jeannie (committee member), Farley, Robert A. (committee member), Tao, Huizhong W. (committee member)

Creator Email yatangli@usc.edu,yatangli8@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-386227

Unique identifier UC11296552

Identifier etd-LiYatang-2420.pdf (filename),usctheses-c3-386227 (legacy record id)

Legacy Identifier etd-LiYatang-2420.pdf

Dmrecord 386227

Document Type Dissertation

Format application/pdf (imt)

Rights Li, Yatang

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA