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Cell-type specialization of layer 5 excitatory neuron functions in tactile behavior underlying object localization

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Cell-type specialization of layer 5 excitatory neuron functions in tactile behavior underlying object localization

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Content Copyright 2023 Samson Garret King
Cell-type specialization of layer 5 excitatory neuron functions in tactile behavior underlying
object localization
by
Samson Garret King
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)
December 2023

ii
ACKNOWLEDGEMENTS
I would like to first thank my advisor, Dr. Samuel Andrew Hires, for the years of guidance and
support while working on my thesis. The process of working in his laboratory has been a
remarkable one, filled to the brim with personal and professional transformations. I would also like
to extend my thanks to my committee members Dr. David McKemy and Dr. Radha Kalluri.
Thank you to all my colleagues over the years: Jinho Kim, Jonathan Cheung, Andrew Erskine,
Skyler Schuyler, Lily Zou, Phillip Maire, Stef Walker, Tanisha London, Chao Wang, Anya Hwang,
Isaac Cohen, and to the several undergraduates and mentees that I have had the pleasure to
(try) and guide. Several of you have influenced me deeply and continue to be sources of great
knowledge.
Thank you to all of my friends who have joined me at various times along the way. These
include (but are not limited to) Kwok Im, Chris Ventura, Sadhna Rao, Charles Lee, Jen Do, Alexis
Gorin, Veronica Scerra, and Anirudh Sattiraju.
Finally, I would like to thank my elder sister, Treasure King, and my father Jeffery D. King.

iii
TABLE OF CONTENTS
Acknowledgments......................................................................................................................................... ii
List of Figures................................................................................................................................................ v
Abstract........................................................................................................................................................ vi
Chapter 1 – Introduction...............................................................................................................................1
Spatial Sampling through Whisking Behavior in Mice ..............................................................................1
The Propagation of Whisker Self-Motion and Touch Information to Cortex ...........................................2
Local circuitry within primary somatosensory cortex...............................................................................3
The circuitry and computational role of Layer 5b pyramidal tract neuronal populations...................4
The circuitry and computational role of Layer 5a intratelencephalic neuronal populations...............5
Potential roles of L5 excitatory cell types in object localization in S1......................................................7
Roadmap...................................................................................................................................................7
Chapter 2 – Layer 5 Cell-type Functions for Object Localization..................................................................9
Introduction ..............................................................................................................................................9
Results.....................................................................................................................................................10
Experimental Design ...........................................................................................................................10
Whisking-mediated activity ................................................................................................................13
Free-Whisking Kinematics...................................................................................................................14
Touch Responses.................................................................................................................................18
Adaptation to Touch ...........................................................................................................................20
Object Location Responses.................................................................................................................21
Discussion................................................................................................................................................23
Summary of Results ............................................................................................................................23
Advantages and Limitations of Approach...........................................................................................24
Touch Responses in IT Cells ................................................................................................................25
Decoding Accuracy of Object Location and the Role of the IT Population.........................................25
Conclusions.........................................................................................................................................26
Materials and Methods...........................................................................................................................26
Lead contact and materials availability...............................................................................................27
Ethics statement .................................................................................................................................27

iv
Experimental model and subject details.............................................................................................27
Object-localization task.......................................................................................................................27
Behavior, videography, and electrophysiology...................................................................................28
In vivo loose-seal juxtacellular recordings..........................................................................................29
Quantification and statistical analysis ................................................................................................30
Defining touch-response window.......................................................................................................30
Tuning Curves......................................................................................................................................30
Neural decoding..................................................................................................................................32
Supplemental Figures .............................................................................................................................32
Author Contributions..............................................................................................................................37
Chapter 3 –Conclusion................................................................................................................................38
References ..................................................................................................................................................40

v
LIST OF FIGURES
Figure 1. Head-fixed task, Circuitry, and Cell Depth. ..................................................................................11
Figure 2. Whisking Modulation of Firing Rate ............................................................................................13
Figure 3. Whisker Kinematic Variable Spiking Modulation.........................................................................14
Figure 4. Population Modulation and Correlation Between Kinematic Variables......................................16
Figure 5. Touch-evoked Spiking Responses................................................................................................17
Figure 6. Adaptation to touch count and Inter-touch-interval...................................................................19
Figure 7. Responses to Object Location......................................................................................................20
Figure 8. Decoding of Object Location by Cell Type ...................................................................................21
Figure S1 Possible trial outcomes and intrinsic signal imaging...................................................................32
Figure S2. Opto-tagging results and waveform criteria..............................................................................32
Figure S3. Session Mean Firing Rates..........................................................................................................33
Figure S4. Absolute and Relative Angle Modulation vs. Phase, Amplitude, and Midpoint........................33
Figure S5. Percent-increase of firing rate above baseline ..........................................................................34
Figure S6. Touch-count-sorted rasters........................................................................................................35
Figure S7 ITI touch responses.....................................................................................................................35
Figure S8. Mean adaptation ratios across all touch counts........................................................................36

vi
Abstract
Layer 5 is the canonical output layer of sensory cortex. The two most numerous neural
constituents of Layer 5 are pyramidal tract (PT) and intratelencephalic (IT) neurons. These
output cell classes combine diverse sets of inputs and project to distinct locations across the
brain, suggesting differing roles in sensory information processing. Here, we investigated the
representation of touch and whisker motion in these two cell types using optogenetically
targeted single unit electrophysiology during whisker-guided object localization. PT neurons (N
= 32) had much higher spike rates than IT (N=26) during behavior. Individual members of both
were modulated by, but average population firing rates were stable between quiet and whisking
periods. PT neurons showed greater absolute spike rate changes, but less relative modulation
than IT neurons to whisking kinematic features. Touch-excited PT (N = 18) and IT neurons (N =
8) rapidly adapted to active touch. Both populations encoded the azimuthal position of touched
objects, with IT neurons more sharply tuned to position. However, position was more precisely
decodable from PT population activity, due to their higher firing rates. A consequence of these
characteristics is that PT neurons, with their higher firing rates, may be more effective
participants in rate-based neural codes, while IT neurons, with their sharp modulation, may be
more effective in timing or synchrony based codes.

1
Chapter 1 - Introduction
The brain's ability to localize objects in space is a fundamental function that remains poorly
understood with respect to touched-object. Several lines of inquiry have implicated a series of
cell-type candidates in S1, but no definitive identified single cell sub-type that is indispensable in
the construction of object location has been described. Here, we seek to better understand the
roles that two major cortical output cell types, PT and IT neurons, have in object localization in S1
of the mouse. By leveraging cell-specific approaches, we can more granularly describe how the
circuit might process touch and self-motion in service of constructing stable, accurate object
location representations
1.1 Spatial Sampling through Whisking Behavior in Mice
Whisking is one of the primary ways that mice spatially sample their environment. The
whisk cycle is an active process relying on the rhythmic actuation of motile vibrissae (whiskers)
on the face to move them through space. A single cycle requires serial contraction of specific
muscles to move the mystacial pad forward to protract (m. nasalis) the whiskers, followed by
further forward motion and rotation enacted by intrinsic sling muscles surrounding each follicle,
and then finally a retraction phase (m. nasolabialis and m. maxillolabialis) (Hill et al., 2008). This
serial, triphasic activation serves to move the whisker in three dimensions, sweeping both forward
and backward through the azimuthal plane as well as rotating in space around its own axis. This
movement pattern is critical to resolving more complex object features contacted, since it allows
sampling the spatial locations between whisker vibrissae that would not be accessed with nonmotile whiskers (Knutsen et al., 2008; Huet & Hartmann, 2014).
High-speed videography and automated tracing of the whisker allow measurement and
characterization of forces acting on it during free, unimpeded whisking as well as during contact
with an object (Clack et al., 2012). Angle at the base of the follicle (θbase) is then obtainable through
application of a Butterworth filter, to which a Hilbert transformation can be applied to extract out

2
midpoint (θmid), amplitude (θamp), and phase (ϕ) (Hill et al., 2011; Huber et al., 2012). Velocity and
change in curvature (Δκ) (Birdwell et al., 2007; Knutsen & Ahissar, 2008) are also extractable. All
of these variables have demonstrated importance for animals deducing the position of a pole
either in the azimuthal (Knutsen et al., 2006; Mehta et al., 2007; O’Connor et al., 2010a; Cheung
et al., 2019) or radial (Szwed et al., 2006; Birdwell et al., 2007; Pammer et al., 2013) plane.
1.2 The Propagation of Whisker Self-Motion and Touch Information to Cortex
Upon whisker movement or contact with an object in the periphery, concomitant axial and
lateral stresses on the follicle excite the trigeminal ganglion (TG) neurons (Zucker & Welker, 1969;
Gibson & Welker, 1983; Stüttgen et al., 2008). Homotopically-clustered TG neurons are
responsive to the motion of single whiskers only, and maintain this homotopy from
mechanoreceptor to layer 4 (L4) barrels of S1 (Woolsey & Van der Loos, 1970; Van der Loos,
1976; Ma & Woolsey, 1984). However, this information is split into three main ascending
pathways: lemniscal, extralemniscal, and paralemnsical.
The lemniscal pathway is formed from trigeminothalmic neurons in the principal trigeminal nucleus
(PrV) that are either physically small and carry single-whisker or large and carry multi-whisker
information to the ventral posterior medial nucleus of the thalamus (VPM) (Veinante & Deschênes,
1999; Minnery & Simons, 2003). The single-whisker cells are the larger of the two classes
numerically, accounting for the majority of cells emanating from PrV, and project a homotopic
barreloid map of the whisker field onto contralateral VPM. Within each barreloid, VPM relay cells
provide a low-convergence, low-divergence method of conveying touch and whisking information
to the cortex, with only one or two single-whisker PrV neurons innervating a single relay cell
(Castro-Alamancos, 2002). The larger, multi-whisker cell type originates within the spaces, the
septa, in between barrelettes in the PrV and ultimately projects homotypically to the septa
between barrels of L4 of S1 (Lo et al., 1999; Furuta et al.,2009).
The extralemniscal pathway is formed by projections from the caudal division of the spinal
nucleus interpolaris (SpVIc) and project to a ventrolateral subdivision of the VPM before

3
continuing on to its primary target in secondary somatosensory cortex (S2) and its much sparser
secondary target in S1 (Pierret et al., 2000). This pathway remains somewhat poorly
characterized functionally, with no awake, behaving studies having been performed directly
addressing its role in the whisker-mediated active-touch system (Yu et al., 2006; Haidarliu et al,
2008). However, the anesthetized-preparation studies that have been performed have implicated
this pathway in conveying whisker contact information (Yu et al., 2006).
The final primary pathway conveying information from the mechanoreceptors to the cortex
is the paralemniscal pathway. This pathway is formed from multi-whisker cells of the rostral
division of the SpVI (SpVIr) and projects to the posterior medial thalamus (POm) (Lavallée et al.,
2005). From there, this pathway projects widely to many cortical targets including L1 & L5a of S1,
S2, primary motor cortex (M1) and secondary motor cortex (M2) (Ohno et al., 2012) and
subcortical targets such as the striatum. Whisker touch responses recorded from POm cells are
weak and long latency, largely due to extra-thalamic inhibitory (ETI) nuclei such as zona incerta
(ZI) receiving parallel input that then inhibits the whisker-responsive synapses in POm (Veinante
et al., 2000; Bartho et al., 2002). Free-whisking is likewise not well-represented in this pathway
(Moore et al., 2015), so the precise nature of the ascending pathway’s role in whisker-mediated
behavior is still unknown. However, POm may play a role as an early convergent zone for
ascending whisker information and descending PT excitatory input (Groh et al., 2014), though the
degree of overlap is still not entirely clear.
1.3 Local circuitry within S1
Single-whisker input to S1 is canonically described as arriving from VPM and innervating
L4 barrels filled with spiny stellate cells (Woolsey and Van der Loos, 1970; Lübke et al., 2000).
These cells are highly recurrent with ~25% of them reciprocally connected (Feldmeyer et al.,
1999; Petersen & Sakmann, 2000). From L4, the canonical circuit follows a path up to layer 2/3
(L2/3) before projecting down to the major output layer, L5 (Gilbert & Wiesel, 1979; ArmstrongJames et al., 1992; Douglas & Marin 2004; Callaway, 2004). This follows a simple input-

4
modulation/learning-output formula. While the cortical column still receives the most overall
excitatory input from L4 neurons (Lefort et al., 2009), this circuit description is a drastic
oversimplification of the system.
S1 receive VPM input to multiple layers. It receives strong thalamic input to L3, L4, L5
(Petreanu et al., 2009), and L6a (Crandall et al., 2017). From here, each thalamic input layer has
its own projection pathway. L3 pyramidal cells appear to provide the most output to more
superficial L2 cells (Shepherd & Svoboda, 2005; Bureau et al., 2006) with additional output
traveling to IT cells in L5a and PT cells in L5b. L4 does not just target L2/3, but also L5a and L5b.
While many of L4’s projections are excitatory, it appears to impart a mixed excitatory/inhibitory
influence via its descending projection to L5a (Feldmeyer et al., 2005; Pluta et al., 2015). L6a
receives VPM and POm input (Cruikshank et al., 2010; Wimmer et al., 2010) selectively to the
corticocortical (CC) cells situated between its own set of barreloid structures, ‘infrabarrels,’ which
principally house corticothalamic (CT) cells (Crandall et al., 2017). CT cells have well-described
projections to L4 (Lee & Sherman, 2009; Thomson, 2010; Pichon et al., 2012) and to L5a (Kim et
al., 2014; Yang et al., 2022). CC cells project across several barrel columns, but the true extent
of their projection pattern is not well-described (Egger et al. 2020).
1.3.1 The circuitry and computational role of Layer 5b PT populations in S1
Layer 5b receives early, strong input from VPM (Petreanu et al. 2009; Constantinople &
Bruno, 2013). PTs within L5b also receive a broad array of cortical input from other cortices’ IT
neurons, much of which is targeted to their L1 distal apical tufts. These tufts are electrotonically
isolated from the soma, but can have their membrane excitability altered via back-propagating
action potentials (bAPs) originating from the soma (Stuart & Sakmann, 1994; Stuart et al., 1997).
These bAPs play a role in aiding the generation of calcium spikes (Yuste et al., 1994) through
potentiating voltage-sensitive calcium channels at times coinciding with distal tuft input (Schiller
et al., 1997; Larkum et al., 1999; Larkum et al., 2004). These calcium spikes can then travel to
the soma and induce burst spiking. This attribute of PT neurons gives them the ability act as

5
coincidence detectors in the cortex, with the electrotonic distance between their distal tufts and
their soma being a strong gate only openable through coincidental input to soma and distal tuft.
PT cells in layer 5 likely contribute very little to local computations within the cortical
column, as their axons seldom branch before they leave cortex (Narayanan et al., 2015).
However, L5b PTs have several presynaptic partners from which they integrate local
computations (Harris & Shepherd, 2015). L4 input is centered primarily on L5b PTs’ basal
dendrites, while L2/3 input projects to upper basal and apical oblique dendrites. These dendritic
domains have been shown to play roles in integrating different sensory, motor, and thalamic
information streams (Lafourcade et al., 2022). This is likely a general property of PTs’ dendritic
domains, as they span across most laminae in cortex.
PT cells project extensively to many subcortical targets such as POm and striatum. PTs
projecting to POm do so with low divergence and low convergence. A single POm neuron receive
input from two to three L5b PT cells and each PT cell will only project to a maximum of eight POm
neurons; PT→POm projections may form a conserved topographic organization from S1 (Sumser
et al., 2017). Beyond that, the projections to POm from PT cells of L5b are some of the strongest
descending inputs from cortex to subcortical targets, likely owing to the giant boutons that
subserve the connection (Hoogland et al., 1991; Reichova, 2004; Sumser et al., 2017; Prasad et
al., 2020) and can induce bursts of spikes in POM cells (Seol & Kuner, 2015; Mease et al., 2016).
This is potentially quite interesting, as POm goes on to project to several targets that are relevant
to whisking behavior and touch including M1 – to which it may be able to transfer a faithful
representation of S1 barrel field – and back to S1, primarily to layers 1 and 5a. The projections to
L1 and L5a could directly affect PTs own excitability or encoding.
1.3.2 The circuitry and computational role of Layer 5a IT populations in S1
L5a receives subcortical input via the paralemniscal pathway (Ohno et al., 2012), though
much of its input from POm may come as a result of descending PT input. However, it has been
previously reported that L5a receives sparse VPM input (Bureau et al., 2006, Petreanu et al.,

6
2009) which could explain the sharp receptive fields reported in ITs (Manns et al., 2004). A large
amount of ITs’ input comes in the form of feedback projections from other distant IT cells (Mao et
al., 2011; Kim et al., 2015), with a preference toward targeting IT cells that then project back to
the source region (Young et al., 2021). This distributed network is one of the principal means by
which cortices communicate with one another, and its recurrent nature has also been posited to
support attractor states (Inagaki et al., 2022) in the cortex. Examples of this would be ITs’ role in
working memory (Bae et al., 2021) as well as in preparatory activity before a movement (Turner
& DeLong, 2000; Baker er al., 2018), both of which have been described as attractor-like states
(Wimmer et al., 2014; Inagaki et al., 2019). Furthermore, ITs’ propensity for spike adaptation
(Hattox & Nelson, 2007) lends itself well to learning models (Salaj et al., 2021) which has been
demonstrated in a learned motor movement task (Shinotsuka et al., 2023). ITs have, however,
been implicated in forelimb movement amplitude as well, but whether this was an artifact of
learning the forelimb movement task or if it was an intrinsic responses is unknown (Park et al.,
2022).
L5a ITs send and receive several local projections. They receive direct excitatory L4 inputs
(Feldmeyer et al., 2005; Petreanu et al., 2009) and indirect inhibitory input from L4 via its effects
on L5a FS cells (Pluta et al., 2015). Additionally, L6 CT cells directly excite L5a ITs while providing
inhibition to L4 (Kim et al., 2014). The actions L4 and L6 on L5a might provide a means of
temporally shaping the responses in L5a while providing a means to amplify existing responses
received from sparse lemniscal input (Bureau et al., 2006). If so, this may have implications for a
described role that IT activity has in sharpening spike timing and frequency tuning in primary
auditory cortex (Onodera & Kato, 2022), in that this sharpening may ultimately be dependent on
precise excitation and inhibition of L4. L5a ITs also send unidirectional projections to L5b PTs
(Kiritani et al., 2012; Groh et al., 2010; Anderson et al., 2010; Brown & Hestrin, 2009). The
functional significance of this projection is unknown.

7
1.4 Potential roles of L5 excitatory cell types in object localization in S1
L5 excitatory neurons have previously shown the ability to represent the position of objects
in space (Cheung et al., 2020). Both L5 cell subtypes receive VPM input (Bureau et al., 2006;
Petreanu et al., 2009), indicating that ITs and PTs have access to low-latency, high-fidelity
representations of homotypically-conserved touch. What are the unique, indispensable roles that
each cell excitatory L5 type might play in the construction object localization in S1? First, since
layer 5 PT cells are well positioned downstream of all cell types in the column including IT cells,
it is likely that they hold the ‘most complete’ form of object location encoding in S1 that is read out
by subcortical targets for further use. The broader-tuning of PT cells may also allow for better
estimation of object location with fewer cell members, as more sharply-tuned IT cells may not
have sufficient sampling of space until very large numbers of cells are used to construct location.
ITs’ role in object localization in S1 is more nebulous when considering the literature and existing
data within the lab. IT cells may have a role in S1 influencing tuning of object location similar to
their role in auditory cortex (Onodera & Kato, 2022), since the reciprocal projection between L2/3
and L5a appears conserved (Feldmeyer et al., 2012). ITs may alternatively have a different role
in S1 compared to auditory cortex, playing a greater role in sensory representation (Park et al.,
2022) or learning (Shinotsuka et al., 2023), as in M1. It could also be that they have no one
indispensable role, instead forming a heterogeneous population that are used as ‘linkages’
between other computational units in the distributed cortical circuit. Too much remains unknown
about their task-related activity in awake, behaving animals, so their role in the circuit remains
unclear.
1.5 Conclusion/Roadmap
Previous work in the lab has yielded insight into L5 excitatory neurons’ role in encoding
object location through whisker-mediated touch. In Chapter 2, we continue probing the circuit to

8
deduce precisely how candidate cell-types in L5 might construct object location in S1. This is
followed by a brief discussion and concluding remarks in Chapter 3.

9
Chapter 2 – Layer 5 Cell-type Functions for Object Localization
Introduction
The brain's ability to localize objects in space is a fundamental function. Precisely how
object localization is encoded at the level of the cortex is still unknown. For touch-guided
localization in rodents, one of the potential ways the brain may do this in the primary
somatosensory cortex (S1) is by integration of an efference copy of motor information arriving
from a separate pathway such as through primary motor cortex (M1) that could disambiguate the
precise location of a touched-object with whiskers (Hill et al., 2011). Alternatively, all information
necessary for disambiguating touch could arise from direct projections from the ventral
posteromedial nucleus (VPM) to laminae where touched-object location is encoded (Hires et al.,
2015; Petreanu et al., 2009; Armstrong–James et al., 1992; Fee et al., 1997).
In either case, the cells most likely to faithfully encode touched-object location are layer 5
(L5) excitatory neurons, particularly emphasizing L5 pyramidal tract (PT) cells. Neurons of this
class receive broad, diverse inputs from cortical (Lefort et al., 2009; Hooks et al., 2011) and
sensory input from subcortical (Petreanu et al., 2009) sources and possess location-dependent
information represented in their distal apical tufts (Xu et al., 2012; Petreanu et al., 2012) and soma
(Cheung et al., 2020; Ranganathan et al., 2018). They receive local input from several other layers
in the cortex but likely contribute little to local computations in the circuit since their axons seldom
branch within the cortex (Narayanan et al., 2015). PTs project to many subcortical targets (Guo
et al., 2017) such as the striatum (Hooks et al., 2018; Hintiryan et al., 2016; Wall et al., 2013) and
the posterior medial thalamic nucleus (POm) (Sumser et al., 2017; Groh et al., 2014). This
placement in the circuit makes PT neurons uniquely well-suited to integrating sensory information
required for disambiguating touched-object location.

10
Previously, we were able to show that L5 excitatory neurons in S1 had preferred-objectlocation touch responses that could tile the available sample space. Furthermore, location-specific
responses from this dataset were used to construct a decoder that could accurately (60.5 ± 1.3%
at ≤ 0.5-mm distance) predict the actual location of a pole (Cheung et al., 2020). However, our
previous approach did not adequately differentiate between the candidate cell type we think most
likely to represent object location in S1, L5 PT neurons, and the other major output cell type in
L5, intratelencephalic (IT) neurons.
Here, we use two mouse line crosses that selectively express ChR2(H134R) in L5 PTs or
L5 ITs to opto-tag and disambiguate the cell identity of the L5 pyramidal cells collected. Following
our identification, we employ single-unit juxtacellular electrophysiology to investigate neural
representations of whisking and touch during active behavior. We find that PTs and ITs had many
units that were either positively or negatively modulated by whisking, indicating that both cell types
can encode self-motion. Both cell types also displayed units that responded to touch at similar
latencies and for similar durations, with PTs displaying much larger absolute touch responses. At
the same time, many ITs showed a greater percent increase above their on-average low firing
rate. ITs and PTs exhibited touch adaptation to serial pole sampling within a given trial and had
attenuated responses at smaller inter-touch intervals (ITIs); however, touch-excited ITs did have
a more rapid drop-off in firing rate after the first touch. PTs and ITs could also decode object
location, with PTs outperforming ITs and both cell types outperforming a mixed cell-type decoder
at smaller neuron pools. These data suggest that PTs and ITs can represent object location well
enough to potentially decode it but that PTs may be more specialized for this role. At the same
time, ITs may be excellent at detecting contextual state changes but less suited for other roles.
Results
Experimental Design
To investigate the specialization of excitatory layer 5 cell-type functions in tactile sensation
and object localization, we combined loose-seal juxtacellular electrophysiology and high-speed

11
videography during behavior. Water-restricted mice (n = 23) were trained to perform a single
whisker-guided go/no-go object localization task (Cheung et al., 2019). Mice were required to
investigate the position of an actuated pole and report their percept via a lick to attain a water
reward or withhold licking to avoid a time-out (Figure 1A, B, S1A). Whiskers were tracked at 1000
frames-per-second (fps), traced, and decomposed into constituent whisker kinematic variables
(Materials and Methods).
Loose-seal (~7.6 MOhms) juxtacellular electrophysiological recordings were performed
following intrinsic signal imaging targeted to the C2 primary whisker barrel and subsequent
craniotomy (Figure S1B, C). All recordings were performed using one of two mouse line crosses,
each of which respectively expressed channelrhodopsin-2 (ChR2-H134R) in either layer 5 PT
(Sim1-cre x Ai32, n = 14 mice) neurons or layer 5 IT (Tlx3-cre x Ai32, n = 9 mice) neurons (Gerfen
et al., 2013; Figure 1C, D). We recorded a total of 135 neurons between the two mouse lines (73
Sim1-cre x Ai32 / 62 Tlx3-cre x Ai32), which were opto-tagged using 472 nm pulsed (10Hz, 10ms
width) light directed onto the site of the craniotomy (~200 µm). Since there is a known
unidirectional projection from IT neurons to PT neurons (Kiritani et al., 2012; Groh et al., 2010;
Anderson et al., 2010; Brown & Hestrin, 2009), we employed stringent post-illumination spike
latency (time to first spike <= 5.6 ms) and waveform characteristics (Methods; Figure S2A-D).
Following this approach, we were left with a population of 32 putative PT and 26 putative IT

12
neurons. Recordings of PT neurons spanned a range of depth from pia of (570-986 µm, mean
751.5 µm), while IT neurons were somewhat shallower on average (498-922 µm, mean 742.2
µm; Figure 1E). Across the recording session, the PT population had significantly higher mean
firing rates (population mean: 17.42 ± 3.53 spk/s) than the IT population (population mean: 2.23
± 0.73 spk/s; p = 9.91e-11, Two-Sample Kolmogorov-Smirnov Test) (Figure S3), with no
discernable trend of either with estimated cell body depth. This difference in firing rate
demonstrates that the greater excitability of PT over IT neurons (Hattox & Nelson, 2007; Kasper
Figure 1. Head-fixed task, Circuitry, and Cell Depth. (A) Task schematic. To receive a reward, mice
are trained to discriminate between a near (go) and far (nogo) range. Opto-tagging was performed via
10Hz, 10ms-wide pulses of 473 nm light prior to further data collection upon finding a cell to determine
identity. (B) Trial structure and aligned data streams. Once recording had begun, high-speed (1000 fps)
videography and precise electrophysiology were collected alongside behavior. The trial structure
differed in ending times depending on the choice selection behavior of individual mice. (C) Histology
showing expression of ChR2(H134R)-eYFP in either PT (top, Sim1-cre x Ai32 cross) or IT (bottom,
Tlx3-cre x Ai32 cross) cells. (D) Circuit diagram displaying some of the input and output pathways for
L5 excitatory cell types in S1. Modified from Lefort et al., 2009. (E) Depths of collected cells plotted
alongside their mean firing rate (top). The depth distributions of PT cells (black, mean depth 751.5 µm,
range 570-986 µm) and IT cells (red, mean depth 742.2 µm, range 498-922 µm ) are shown in the
middle and bottom subplots, respectively.

13
et al., 1994) and their propensity for different firing modes (Chagnac-Amitai et al., 1990) translate
into higher relative firing rates for PT neurons when these cortical circuits are engaged in active
tactile exploration.
Whisking-mediated activity
Given the distinct input circuitry to PT and IT neurons in S1 (Mao et al., 2011;
Constantinople & Rudy, 2013) (Figure 1D), we reasoned that there might be fundamental
differences in their responsiveness to whisker motion when viewed independently rather than as
a combined class of cells. This notion is supported by previous work in which putative ITs in upper
L5a of rat cortex displayed enhanced firing rates compared to PTs during whisking periods with
no contacts (de Kock & Sakmann, 2009). For both classes, the majority of identified neurons
showed significant modulation to whisking (PT 29/32; IT 21/26 chi-squared test, 95%
significance), with a balanced distribution of neurons that were positively versus negatively
modulated (Figure 2A, B). However, for both cell types, the net change in population spiking
between quiet and whisking periods was not significantly different from zero (PTs: 0.83 ± 5.41Δ
spks/s p = 0.33; ITs: 0.067 ± 1.82 Δ spks/s p = 0.38, Wilcoxon signed-rank test Figure 2A-C).
The absolute modulation expressed as changes in spike rate was significantly larger in PT than
in IT neurons (Figure 2B, C; 3.9 spks/s for PTs vs 1.1 spks/s for ITs p = 2.51e-05, Mann-Whitney
U Test). However, IT neurons were more relatively modulated than PTs (mean 0.32,

14
median 0.28, modulation depth for ITs vs. mean 0.16, median 0.11 modulation depth for PTs p
= 0.01, Mann-Whitney U Test; Figure 2D, E). Overall, the gross firing characteristics of PT and IT
neurons between quiet and whisking periods primarily differed in the mean spike rate of the two
populations rather than modulation characteristics.
Free-Whisking Kinematics
Despite the absence of change in population-averaged spike rates between quiet and
whisking periods, the timing of spikes during whisking could be structured with respect to the
Figure 2. Whisking Modulation of Firing Rate. (A) Schematic, y-axis, displaying whisking. Schematic,
x-axis, displaying quiescence. Firing rate for non-whisking (PTs: 16.30 ± 9.93 spks/s; ITs: 2.23 ± 1.70;
mean + sd) and whisking periods (PTs, black: 17.14 ± 10.59 spks/s; ITs, red: 2.30 ± 2.67 spks/s; mean
± sd) PTs: p = 0.33, z-value = 0.97, signed rank = 316; ITs: p = 0.38, z-value = -0.88, signed rank =
141, Wilcoxon signed-rank test). Significantly whisking-modulated cells (Methods and Materials) are
displayed as filled circles, while other circles representing cells are left open. (B) Absolute modulation
depth, displayed as the firing rate during quiescent periods subtracted from the firing rate during active
whisking periods (PTs, black; ITs, red). (C) As in (B), except all values are displayed as absolute values.
Means for each population are displayed as diamonds in their respective colors (PTs, black: 3.9 spks/s;
ITs, red: 1.1 spks/s, p = 2.51e-05, z-value = 4.21, rank sum = 1214, Mann-Whitney U Test). (D)
Modulation Depth (see Materials and Methods) comparing the modulation of cells between whisking
and quiescent periods. PT cells’ modulation depths are displayed in black, IT cells’ modulation depths
are displayed in red. Bin width for visualization: 0.1. (E) As in (D), except all values are displayed as
absolute values. Means for each population are displayed as diamonds in their respective color (PTs,
black: 0.16 mean/0.11 median modulation depth, ITs, red: 0.32 mean/0.28 median modulation depth,
p=0.01, z-value = -2.51, rank sum = 783, Mann-Whitney U Test).

15
whisker angle and associated kinematic variables (amplitude, midpoint, and phase; Figure 3A).
Tuning curves of single cells (Figure 3B) showed significant angle (PTs: 8.81 mean Δ spks/s; ITs:
mean Δ 1.63 spks/s) and midpoint (PTs: 8.93 mean Δ spks/s change; ITs: 2.01 mean Δ spks/s
change), and less modulated by amplitude (PTs: 6.29 mean Δ spks/s; ITs: 1.37 mean Δ
Figure 3. Whisker Kinematic Variable Spiking Modulation. (A) Schematic of a mouse (left) sweeping
its whisker back and forth during an active whisking period with the concomitant forces displayed
alongside. On the right, whisker kinematics extracted using a Hilbert transform are displayed, with scale
bars relevant to each. (B) PT example unit (top) and IT example unit (bottom), each showing their
respective tuning curves to angle, phase, amplitude, and midpoint. (C) Absolute modulation of PTs
(black) and ITs (red) to each kinematic variable displayed as a swarmplot. Means for each group are
displayed as a large circle with a cyan fill. All statistical comparisons use a Mann-Whitney U Test.
Angle: PT mean = 8.15 Δ spks/s, IT mean = 1.62 Δ spks/s; p = 6.59e-08, z-value = 5.40, rank sum =
1290. Phase: PT mean = 4.60 Δ spks/s, IT mean = 0.90 Δ spks/s; p = 1.41e-06, z-value = 4.82, rank
sum = 1253. Amplitude: PT mean = 5.90 Δ spks/s, IT mean = 1.32 Δ spks/s; p = 2.18e-07, z-value =
5.18, rank sum = 1276. Midpoint: PT mean = 8.40 Δ spks/s, IT mean = 1.88 Δ spks/s; p = 5.41e-07, zvalue = 5.01, rank sum = 1265. (D) Relative modulation of PTs (black) and ITs (red) to each kinematic
variable displayed as a swarmplot. Means for each group are displayed as a large circle with a cyan
fill. All statistical comparisons use a Mann-Whitney U Test. Angle: PT mean modulation depth = 0.29,
IT mean modulation depth = 0.47; p = 3.5e-03, z-value = -2.92, rank sum = 757. Phase: PT mean
modulation depth = 0.17, IT mean modulation depth = 0.33; p = 5.5e-03, z-value = -2.78, rank sum =
766. Amplitude: PT mean modulation depth = 0.22, IT mean modulation depth = 0.38; p =1.7e-03, zvalue = -3.13, rank sum = 743. Midpoint: PT mean modulation depth = 0.48, IT mean modulation depth
= 0.30; p = 8.44e-04, z-value = -3.34, rank sum = 730.

16
spks/s) and phase (PTs: 5.05 mean Δ spks/s; ITs: 0.8967 mean Δ spks/s) (Figure 3C). Overall,
PT neurons had significantly greater absolute modulation (angle: p = 6.59e-08; phase: p = 5.07e08; amplitude: p = 1.21e-06; midpoint: p = 1.56e-07, Mann-Whitney U-test; Figure 3C), while IT
neurons showed significantly higher relative modulation to each kinematic variable (angle: p =
4.77e-03; phase: p = 2.02e-03; amplitude: p = 2.48e-03; midpoint: p = 1.32e-04, Mann-Whitney
U-test; Figure 3D, Figure S4A-C). That midpoint modulation across these parameters for both cell
types provided the greatest modulation depth for both PT and IT neurons was consistent with
prior results in undifferentiated L5 excitatory neurons (Cheung et al., 2020) and underscores its
potential importance in behavior (Cheung et al., 2019).
Within individual PT and IT neurons, there was a substantial degree of variability in
modulation depth across particular kinematic variables; however, at lower firing rates, ITs tend to
have similar modulation depths between different kinematic variables (Figure 4A). Across the
population, PTs’ phase responses correlated the least well with other kinematic variables (angle:
R = 0.67, p = 2.7e-05; amplitude: R = 0.65, p =5.4e-05; midpoint: R = 0.60, p = 2.9e-04, Pearson’s
R correlation). PT’s angle responses, on the other hand, correlated well with the non-phase
kinematic variables (amplitude: R = 0.85, p = 9.1e-10; midpoint: R = 0.84, p = 2.5e-09, Pearson’s
R correlation) (Figure 4B, left). The IT population’s phase-midpoint pair had the lowest correlation
(R = 0.39, p = 0.044), closely followed by its amplitude-midpoint pair (R = 0.48, p = 0.014). Except
for midpoint, ITs cells’ phase correlations with other variables tended to be stronger than those of
PT cells’, with the second strongest association between it and angle (R = .75, p = 1.2e-5). The
strongest association was observed between angle and amplitude (R = 0.79, p = 1.4e-06) (Figure

17
4B, right). Angle and amplitude modulation are well-correlated in both cell classes, while phase
and midpoint are more poorly correlated. The commonalities in co-variation across cell types
could indicate that the pathways carrying slowly-varying (amplitude and midpoint) and quicklyFigure 4. Population Modulation and Correlation Between Kinematic Variables. (A) Relative
modulation of PTs (left) and IT (right) by angle, phase, amplitude, and midpoint sorted by firing rate
(displayed in log scale at the bottom for each cell type). (B) Contingency tables of Pearson correlation
coefficients for modulation depths across angle, phase, amplitude, and midpoint for PTs (left) and ITs
(right).

18
varying (angle and phase) kinematic variables’ information converge on both deep L5 output cell
classes.
Touch Responses
Across the recorded populations, 78% (25/32) of PT and 46% (12/26) of IT neurons were
phasically modulated by touch (Figure 5A, B). Of the modulated population of PTs, 72% (18/25)
were excited at the time of touch, and 28% (7/25) were inhibited. A similar percentage of touchmodulated ITs were excited (67%, 8/12) and inhibited (33%, 4/12) by touch. For the touch-excited
Figure 5. Touch-evoked Spiking Responses. (A) Heatmaps (PTs, left column; ITs, right column) of
touch responses aligned to touch times. Cells that were considered ‘excited’ by touch were plotted on
the top, cells inhibited by touch were plotted directly below excited cells, and any cells that had
responses that were either not discernible or not significant (See Methods and Materials) were plotted
at the bottom. (B) Population mean firing rate traces (PTs, left column; ITs, right column) for excited
cells (blue), inhibited cells (red), untuned cells (gray), and all cells (black) aligned to the time of touch
(gray dashed line). (C) Latencies for first spikes after touch for PTs (mean = 12.9 ms, black) and ITs
(mean = 13.6 ms, red),p = 0.48, k-statistic = 0.33, Two-Sample Kolmogorov-Smirnov Test. Means are
shown as diamonds in each cell type’s respective color. (D) Duration of the touch response is defined
as the width of the response window. PTs are shown in black (17.6 ms ±6.26 ms C.I) and ITs in red
(15.1 ± 8.95 ms C.I.), with means displayed as diamonds of each cell type’s color. A Two-Sample
Kolmogorov-Smirnov Test was used to compare the two distributions: p = 0.87, k-statistic = 0.24. (E)
Mean spikes evoked per touch for PTs (black, 0.15 ±0.026 spikes/touch, N = 32) and ITs (red, 0.013
±0.009 spikes/touch, N = 26). (F) Mean additional spikes per touch for protracting versus retracting
touches for PT cells (black, mean = 0.09 Δ spikes, p = 0.06,z-value = 1.87, signed rank = 232 ) and IT
cells (red, mean = 0.06 Δ spikes p = 0.20, signed rank = 56).

19
population of neurons, IT cells (N = 8) and PT cells (N = 18) displayed similar average post-touch
response latencies (12.9 ms for PTs and 13.6 ms for ITs). However, ITs’ median response
latencies (8.5 ms) were faster than PTs’ (11.5 ms) (Figure 5C), suggesting that both cell
populations receive early input from thalamic or local thalamo-recipient cells (Pluta et al., 2015;
Petreanu et al., 2009; Feldmeyer et al., 2005), though the difference between the two cell types
remains non-significant (p = 0.48, Two-Sample Kolmogorov-Smirnov Test). The mean duration of
the response was also similar between the two groups (PTs: 17.6 ± 6.36 ms C.I.; ITs: 15.1 ± 8.95
ms C.I., p = 0.87, Two-Sample Kolmogorov-Smirnov Test) (Figure 5D). PT cells displayed an
average higher number of baseline-subtracted touch-evoked spikes than IT cells (PTs’ 0.15 ±
0.026 spikes/touch vs ITs’ 0.013 ± 0.009 spikes/touch) (Figure 5E). However, IT cells that were
touch-excited displayed an overall higher percent-increase in spiking compared to PT cells (PTs:
101% mean increase above-baseline firing rate, ITs: 149% mean increase above-baseline firing
rate; Figure S5A, B), though the difference was not significant (p = 0.12, Two-Sample Kolmogorov
Smirnov Test). However, this difference was enhanced when comparing only the first touches in
trials (PTs: mean 341% vs. IT mean 1361%), though these two distributions were still not
significantly different (p = 0.25, Two-Sample Kolmogorov Smirnov Test). Protraction touches
elicited more spikes from both PT and IT populations compared to retraction touches, though not
significantly so (PT: mean = 0.09 Δ spikes, p = 0.06, ITs: mean = 0.06 Δ spikes, p = 0.20, Wilcoxon
Signed-Rank Test) (Figure 5F). Overall, PT and IT cells displayed similar spike latencies and
post-touch response durations, and PTs produced significantly more above-baseline spikes
compared to ITs for all touches, with ITs showing a consistently higher percent-increase in their
firing rates for all touches but especially first touches. The prominence of the response to first
touches in the IT population may allow touch events to stand out more strongly than in the PT
population’s higher basal firing rate, making them more sensitive to rapid state changes in the
sensor or context.

20
Adaptation to Touch
Since strong adaptation is a known attribute of IT cells (Guan et al., 2015; Hattox & Nelson,
2007) and has been shown in response to passive whisker deflections at naturalistic frequencies
in rodents (Kheradpezhouh et al., 2017), we next sought to investigate the role that adaptation
plays in each cell type in S1 during active touch behavior. We identified examples of adaptation
in each cell type, with a significant drop-off in evoked spikes between first and subsequent touches
(all touches after the first) in a trial (Figure 6A, Figure S6). This was a general characteristic of
Figure 6. Adaptation to touch count and Inter-touch-interval. (A) Raster example (top) of a PT (left)
and IT (right) sorted by touch order. (Upper-middle) PSTHs for the same example PT (left) and IT (right)
as above. First-touch only is labeled in black, successive touches in gray. (Lower-middle) Adaptation of
the example cells’ respective responses with successive touches from the first touch starting at the left
of each figure and proceeding to the right. (Bottom) Each example cell’s responses to touch following
different inter-touch-intervals. (B) First vs. late touch mean spike count. PTs are displayed on the left as
black dots (mean first = 0.43 spikes/touch; mean late = 0.11 spikes/touch]). ITs are displayed on the
right as red dots (mean first = 0.03 spikes/touch; mean late = 0.01 spikes/touch]). The red dashed box
on the left panel indicates the space occupied by the ITs shown on the right panel. (C) Adaptation to
touches immediately proceeding after first touch for PTs (black, 0.73) and ITs (red, 0.60), p = 0.33, kstatistic = 0.38, Two-Sample Kolmogorov-Smirnov Test. (D) (top-left) Proportion of touches found for
PT cells (blue) at different ITIs, cumulative distribution of all touches across ITIs (red). (top-right) Same
as in the top-left, but for IT cells. (bottom-left) Normalized mean touch responses for all PTs across
different ITIs. (bottom-right) Same as in the bottom-left, but for ITs. (E) Heatmap of binned, normalized
responses to touch ITI (top, PT, N = 18; bottom, IT, N = 8)

21
most touch-excited cells in both populations (16/18 PT; 6/8 IT) (Figure 6B, Figure S7). The mean
adaptation ratios (mean subsequent touch evoked spikes / mean first touch evoked spikes)
between the cell types were similar (0.62 for PTs, 0.57 for ITs, Two-sample Kolmogorov Smirnov
Test) (Figure S8). IT neurons adapted more sharply than PTs, as shown by the smaller evoked
spike ratio between the first touch and second bin of subsequent touches (Figure 6C). However,
this effect was not significant (p = 0.33, Two-sample Kolmogorov-Smirnov Test). Expressing
adaptation in terms of inter-touch interval (ITI; the time since prior touch) showed a monotonically
increasing response (Figure 6A, D, E). Overall, touch-excited PT and IT cells show strong
adaptation to touch in the context of single-whisker tactile exploration, providing a relatively short
window of time to gather information about the characteristics of the touched object.
Object Location Responses
Layer 5 neurons encode the azimuthal position of touched objects (Cheung et al., 2020;
Ranganathan et al., 2018). Is this positional encoding specific to either PT or IT neurons? Sorting
touch evoked spike rasters by pole position revealed members of both populations that showed
enhanced firing in particular regions of the azimuthal plane along the face (Figure 7A). Both PT
(N = 32) and IT (N = 26) cell populations tiled the space from posterior to anterior pole positions
Figure 7. Responses to Object Location (A) PSTHs for an example PT (top-left) and an example
IT (top-right). (bottom) Touch-aligned rasters for each respective example, with trials sorted by pole
location from posterior (1) to anterior (-1). (B) Population heat map of normalized object location
responses for PTs (left) and ITs (right), each sorted by cells’ preferred locations from posterior (1) to
anterior (-1). White spaces are insufficiently sampled pole locations. (C) Shape of normalized tuning
curves across units from each cell type (PTs in black, ITs in red) indicating the mean peak response
at preferred locations.

22
when considering all cells (Figure 7B). When comparing the two populations’ modulation widths,
the IT population displayed a sharper response on average for the preferred pole location. On the
other hand, PTs were more broad in their response to pole location (Figure 7C)
The accuracy of decoding information from population spikes is influenced by rates (higher
rates provide more bit depth) and precision (sharpness of individual tuning curves. The PT
population provides many more spikes than IT, while the average IT neuron is more sharply tuned
(Figure 7C). Which population can be used to extract the position of touched objects from their
touch-evoked spike patterns more faithfully?
Previously, the responses of 15 unlabeled L5 excitatory cells were adequate to predict a
pole’s actual location across space (Cheung et al., 2020). We applied a similar approach
(multinomial generalized linear model; GLM) to each cell class. Models built with PT cells fared
slightly better than their IT cell-only counterparts (PTs: 17.2% / 69.4% / 86.5% vs. ITs: 16.3%
Figure 8. Decoding of Object Location by Cell Type (A) Contingency tables for pole location
decoding performance for PTs (top-left) and ITs (bottom-left). Final table displayed is for 75 pooled units
using a multinomial GLM. Performance for each cell type as a function of pool neuron count is displayed
to the right of each cell type’s contingency table. (B) Contingency table for a combined (PT and IT)
dataset of 75 pooled units (C) Performance of PT-only (black, gray), IT-only (red, pink), and PTIT-mixed
(blue, light blue) models (blue) at 75 and 15 cell members pools.

23
/67.4% / 85.2% at 0mm / 1mm / 2mm from the actual location, 15 cells each). When larger pools
of neurons were used in each model, the performance gap grew, and the IT model performance
plateaued (PTs: 31.0% / 86.7% / 95.9% vs. 24.9% / 80.2% / 92.8% at 0mm / 1mm / 2mm from
the actual location, 75 cells each) (Figure 8A). There is a possibility that ITs and PTs work in
concert to construct a representation of object location in S1. Therefore, we also trained a model
that drew from both datasets. The result was a model that performed less well than either PTs or
ITs alone at smaller pools of neurons (14.3% / 54.9% / 80.7% at 0mm / 1mm / 2mm from the
actual location, 15 cells) but that improved to similar performance as the other two models when
using larger pools of neurons (30.8% / 80.1% / 94.9% at 0mm / 1mm / 2mm from actual location,
75 cells) (Figure 8B, C). In Summary, both cell types can be used to decode actual pole position
with an accuracy of close to ~70% at 1 mm of actual position with 15 cells; however, the
performance degrades to ~55% when the two populations are combined. This indicates that these
cell types might be able to decode object location better as segregated populations at lower cell
counts. At higher cell counts, the combined cell model recovers to similar performance levels.
Discussion
Summary of Results
We used mouse lines with cell-specific expression in L5 of S1 to further disentangle
sensorimotor representations during a whisker-mediated task (Figure 1). We found that PT cells'
absolute modulation in response to active whisking was much higher during whisking periods, but
ITs had higher relative modulation than during whisking periods (Figure 2). The free-whisking
kinematics of PTs and ITs were somewhat similar, with angle and amplitude modulation wellcorrelated and phase and midpoint modulation more poorly correlated across both cell subtypes
(Figure 3,4). We found the touch-evoked absolute spiking of PTs to be significantly higher than
that of ITs, though ITs might be weakly more selective for protraction touches (Figure 5). We
found that both ITs and PTs adapted to multiple consecutive touches and that touches following
long ITIs elicited the largest touch-evoked spiking; however, several ITs showed higher adaptation

24
between the first touch and touches immediately following (Figure 6). Both cell types displayed
object-location-dependent firing rate changes upon touch, though PTs’ responses were generally
broader but more accurate at decoding the actual location of a pole (Figure 7,8).
Advantages and Limitations of Approach
Our approach gave us a significant advantage regarding the initial classification of cells
based on their responses to opto-tagging (Muñoz et al., 2014); however, it still has several
drawbacks that should be addressed. A standing limitation of our approach is the inability to
recover individual cell morphologies (Egger et al., 2020) due to recording over multiple days. This
knowledge could help in further assessment or sub-classification of the quickly adapting response
seen in several IT cells (Udvary et al., 2022; Oberlaender et al., 2012), for example, and would
also act as a robust secondary identifier of cell type beyond opto-tagging. In addition, our
approach would benefit from adding multi-unit recording techniques that, in tandem with known
behaviors of single units, could be leveraged to make more specific claims about the true
population-level behavior of each cell type. Another major caveat to our approach is the known
unidirectional projection from layer 5 ITs to layer 5 PTs (Kiritani et al., 2012; Brown & Hestrin,
2009; Anderson et al., 2010). We attempted to address this through the stringent application of a
waveform-based criterion for inclusion in the dataset (see Materials and Methods). However, our
approach may not be the best suited to disentangling L5 ITs vs. L5 PTs' unique contributions to
touch-mediated object localization. If the IT → PT projection is functionally important in the circuit,
it would be beneficial to investigate how perturbing or inhibiting IT cells upstream of PTs affects
the ability to localize objects or how this otherwise disrupts whisker-mediated touch. This is
especially important in the context of recently reported findings in which IT cells were shown to
sharpen the frequency and temporal domain of tone-evoked responses in layer 2/3 of the auditory
cortex (Onodera & Kato, 2022). Finally, IT networks have been implicated as potentially crucial to
learning and internal modeling (Salaj et al., 2021). An approach in which single cells may be
tracked over multiple sessions/days as learning occurs, such as chronic multiunit probes (Bimbard

25
et al., 2023; van Daal et al., 2021) or two-photon imaging (Denk et al., 1990; Platista et al., 2023),
might display functions for these cells that we would never see in isolated juxtacellular recordings.
Touch Responses in IT Cells
Several of the individual findings in this dataset came as a surprise within the context of
what was known of ITs’ and PTs’ respective roles within the S1 circuit. Many studies report ITs
as relatively insensitive to whisker touch (Egger et al., 2020; de Kock et al., 2021) and
preferentially responsive to whisking without touch (de Kock & Sakman, 2009). However, ITs were
only relatively more modulated by whisking than PTs (Figure 2). In a few cells, they displayed
robust touch responses relative to the baseline rate and adapted within one subsequent touch
back to near baseline levels (Figure 5). This later finding was peculiar for a couple of reasons.
First, the timescale of the response was similar between ITs and PTs (Figure 4C), which indicates
that ITs were receiving either early direct lemniscal thalamic input as in PTs (Bureau et al., 2006,
Petreanu et al., 2009) or from direct combined excitatory (Feldmeyer et al., 2005) / inhibitory
(Pluta et al., 2015) descending input from L4. The alternative of ITs receiving direct paralemniscal
input (El-Boustani et al., 2020; Groh et al., 2014; Ohno et al., 2012) seems unlikely, as the
timescale would be too long ( Diamond et al., 1992; Sosnick et al., 2001) to appear with the low
latency shown here. A potential explanation for these touch responses may come from rapid, brief
excitation by layer 6 corticothalamic (CT) cells, which weakly disynaptically inhibits L4 and excites
L5a regular-spiking (RS) and fast-spiking (FS) inhibitory cells (Kim et al., 2014). This initial net
excitatory influence on L5a could shape a transient touch response arriving from sparse VPM
input (Bureau et al., 2006) in L5a ITs that is abolished as descending, thalamically-driven L4
inputs inhibits them (Pluta et al., 2015). It is still unclear how these particular touch responses in
ITs arise or what their specific function is.
Decoding Accuracy of Object Location and the Role of the IT Population
That actual object location was decoded as accurately as it was by a model trained with
IT inputs also came as a surprise (Figure 6D). ITs performed more poorly than PTs at every

26
distance from the actual pole position and at every size of neuron pool used in the decoding step,
but generally only by ~5-10 accuracy. Since the circuit already has PT neurons, which can
seemingly do a similar computation more accurately, what could ITs be doing? ITs have been
implicated in learning, forming internal models, working memory (Bae et al., 2021), and
preparatory behavior (Salaj et al., 2021), and a recent study showed that learned- but not
unlearned- motor behaviors were robustly represented in IT cells of M1 (Shinotsuka et al., 2023).
It is possible that ITs’ sharp encoding of preferred object location is partially learned and changes
through exposure to behaviorally relevant tasks and stimuli. With a more longitudinal recording
method, this could likely be readily tested.
Conclusions
Overall, we conclude that PT neurons decode preferred object location more accurately
and respond more robustly to touch stimuli than IT neurons despite displaying more adaptation
than expected on average (Figure 4C, E) (Mason & Larkman, 1990). That aside, their responses
more closely match those of previous unlabeled datasets of L5 excitatory neurons we have
generated (Cheung et al., 2020), supporting them as the candidate cell type most likely to carry
object location information in S1. The method by which they carry this information still needs
additional testing; however with their higher firing rates PTs may be more effective participants in
rate-based neural codes. IT neurons, on the other hand, may be more effective in timing or
synchrony based codes with their sharp, transient modulation. An additional caveat should be
noted, however: since both of the cell-specific mouse lines used are corticostriatal (Gerfen et al.,
2013, Morgenstern et al., 2022; Papale et al., 2023) and not all ITs or PTs project to striatum
(Rojas-Piloni et al., 2017; Kim et al., 2015), there may be populations of intermingled ITs and PTs
that operate under either slightly or fundamentally different spiking regimes that will be overlooked
if using only these lines to investigate these cell classes. Further experiments are needed to say
much more about the role of PTs and ITs in this circuit.
Materials and Methods

27
Lead contact and materials availability
Further information and requests for resources and reagents should be directed to and
will be fulfilled by the lead contact, Samuel Andrew Hires (shires@usc.edu).
Ethics statement
All procedures were approved under USC IACUC protocol 20788 per United States
national guidelines issued by the Office of Laboratory Animal Welfare of the National Institute of
Health.
Experimental model and subject details
Fourteen Sim1-cre x Ai32 crosses specifically expressing channelrhodopsin-2 (ChR2-
H134R) in corticostriatal pyramidal tract neurons, and nine Tlx3-cre x Ai32 crosses specifically
expressing ChR2(H134R) in corticostriatal intratelencephalic tract neurons were used in all
experiments. Individuals within the used population included males and females; all subjects
were at least three months of age. A head-plate implantation procedure was conducted as
described by Guo et al., 2014. Postoperatively, mice were housed with littermates where possible
or singly housed if complications such as fighting occurred. Mice were provided food ad libitum
and water restricted to 1 mL daily for one week before training and recording. A daily health
assessment was completed to ensure that the mice were healthy.
Object-localization task.
Mice were trained in a whisker-based go/no-go object-localization task. Using a single
whisker (C2), water-restricted mice were motivated to whisk and identify the location of a smooth
vertical pole (0.6-mm diameter) 7–12 mm lateral from the whisker pad. The pole moved along the
anteroposterior axis across 12 mm and was positioned using stepper linear actuators with 99-nm
resolution, 25-μm accuracy, and <5-μm repeatability (Zaber NA11B30-T4). To avoid potential
ultrasonic cues associated with stepper motor movement, the pole position was jittered 0–127
micro-steps (0–25 μm) on each trial. A pneumatic linear slider (Festo) was used to raise the pole

28
vertically into touch reach for each trial. The Festo also provided a sound cue on the pole
presentation onset.
Specific pole locations rewarded mice with water (2–8 μL), punished mice with a time-out
(2 s) or had no effect based on the mouse’s decision to lick or withhold licking. In a go/no-go
paradigm, four trial outcomes exist. In a minority of sessions in which the animals were trained,
the close posterior 6 mm of pole locations (go) were rewarded with water rewards upon licking
(hit) or had no effect if mice withheld licking (miss). The far anterior 6 mm of pole locations (nogo) were punished with time-out (false alarm) or had no effect if mice withheld licking (correct
rejection). For the remaining sessions, rewards and punishment were given regardless of the pole
location—go trials and no-go trials had overlapping pole locations.
Behavior, videography, and electrophysiology.
Animal behavior, videography, and electrophysiology were synchronized and captured
during task performance using Wavesurfer (https://wavesurfer.janelia.org/). A single computer
running BControl (MATLAB 2007b) was used to initiate each trial of the object-localization task
and synchronize video and electrophysiology recordings via a second computer running
Wavesurfer. Trial onset triggered high-speed video capture of whisker motion (1,000 fps) and
electrophysiology recording of single-unit activity (MultiClamp 700b).
Whisker motion was captured from an overhead view and spanned 3 s, spanning pole
onset to response window. Video frames were acquired using an Adimec N-5A100-Gm/CXP-6
camera and an Edmund Optics 0.18X 1⁄2” GoldTL Telecentric Lens (Model # 52–258) under 940-
nm illumination on Streampix 7 software. Whisker shape and position were traced and tracked
using Janelia Farm’s Whisker Tracker (https://www.janelia.org/open-science/whisk-whiskertracking). A mask was traced around the edge of the fur to reduce tracking noise. The whisker
angle is quantified at the intersection between the mask and the whisker. The whisker midpoint,
instantaneous phase, and amplitude were decomposed from the bandpass- and zero phase–
filtered (6–60 Hz, Butterworth) whisker-angle time series using the Hilbert Transform (MATLAB

29
2020a/2023b: Hilbert). Whisking amplitude and phase are defined as the magnitude and phase
angle (radians) of the Hilbert Transform of the whisker-angle time series, respectively. A phase
value 0 is the most protracted location of the whisk cycle, π and −π are the most retracted
positions, and the sign of +/− defines retraction or protraction whisking directions. The whisking
midpoint is the filtered (6–60 Hz) difference between the whisker-angle time series and bandpassfiltered signal. Whisker curvature is the amount of bending of the whisker measured 3–5 mm
lateral from the whisker mask. The precise millisecond of touch was determined through an inhouse auto-curation tool, Whisker Automatic Contact Classifier (WhACC) (Maire et al., 2023),
which required a set of manually curated test images comprising whisker interactions with the
pole and whisking in space to achieve high performance.
In vivo loose-seal juxtacellular recordings.
All animals used in this study were adult male or female transgenic mice (Sim1-Cre x Ai32
or Tlx3-cre x Ai32) expressing channelrhodopsin in either pyramidal tract (PT) cells or
intratelencephalic (IT) cells. Following head-plate surgery, mice were trimmed to one whisker
(C2), and intrinsic signal imaging was used to target the associated barrel column. A single
whisker was maintained throughout training and recording. Prior to recording, animals were
anesthetized (2% isofluorane), and a small craniotomy (200–300 μm) was made above the barrel
column associated with the C2 whisker. On the first day of recording, animals were allowed to
recover for one hour before recording. Recordings were repeated for 5.8 ± 5.4 sessions (mean
± SD) per animal.
To sample single-unit spiking activity in a manner unbiased by firing rate, blind juxtacellular
loose-seal patch recordings were targeted to neurons using patch pipettes (Warner Instruments;
5–8 MΩ) filled with 0.9% saline (Growcells). Electrical recordings (n = 135 neurons) were acquired
and amplified using MultiClamp 700b and Headstage CV-7B. The pipette axis was aligned parallel
to the C2 barrel column at 35°. To perform an unbiased sampling of putatively PT neurons or IT
neurons, we recorded from any isolated unit. An isolated unit was identified by an increase in

30
resistance to 13–20 MΩ. Once a unit was isolated, it was opto-tagged at 10Hz frequency for 1.5
ms at 10ms width with bursts of 473 nm light at 9-16 milliwatts (beam width ~200-μm diameter at
skull, UltraLasers Model CST-L-473 nm– 50—OEM). The beam was focused onto the recording
site from overhead and was used to test whether the recorded unit was either a PT neuron (for
Sim1-Cre x Ai32) or an IT neuron (for Tlx3-Cre x Ai32). Stimulation was conducted for 5 trials in
PTs and 1-2 trials in ITs. The ITs were stimulated for fewer trials to avoid an undesired behavioral
response from the mouse characterized by general agitation and distress. The precise
mechanism underlying this response remains unknown; however, multiple repeated stimulations
caused a ‘playback’ of entrained responses often close in timing and frequency to the stimulation.
After opto-tagging, an isolated unit was maintained for up to 300 trials, and all cells were kept for
further analysis if they had at least 70% of all possible pole locations represented in the session.
Quantification and statistical analysis
Defining touch-response window.
A smoothed (Bayesian adaptive regression splines (BARS); Wallstrom et al., 2008)
response −50 ms to 50 ms around touch was used to evaluate the touch-response window. This
window was generated two ways: one with all touches and one with the first touch only for cases
in which first touches are not captured adequately. The touch-response window was defined as
any time point from 5 to 50 ms post-touch in the smoothed response that exceeded baseline (−50
to 0 ms pre-touch) ± the 95% confidence interval. Then, the response windows were manually
curated to best fit the probability density function of touches in cases where the initial approaches
again failed to capture the window of the response. Any cells not assigned a touch response
window due to non-response were assigned a response window that was the median of all
response windows within the dataset.
Tuning curves.
A tuning curve is a response (firing rate) as a function of a stimulus (e.g., whisker position).
For a single neuron, 5% of sampled touches or 5% of total whisking time points were used to

31
define a point along the touch- or whisking-tuning curve. This method ensured 20 equally sampled
bins of stimulus (e.g., whisker position) and response (firing rates) values. The response is
defined as the firing rate within the touch-response window for touch tuning. For whisking tuning,
the same response window as touch was used. If a neuron was not tuned to touch, the median
touch-response window across all neurons was used to evaluate whisking tuning. The median
touch-response window is 10–40 ms post-touch. The stimulus value is defined as the median of
the stimulus in each sampled bin. Response values are defined as the mean of the responses in
each sampled bin. Tuning curves were generated by smoothing the stimulus and response values
using BARS. Neurons that had mean whisking responses less than 1 Hz were not evaluated.
We used a two-step process to define whether a neuron was significantly tuned to a
specific location. We first performed a one-way ANOVA at an alpha level of 0.01 to identify if any
angle/position’s firing rate at touch or during free-whisking significantly differed from another
angle/position’s. If a neuron passed this first test, we moved on to the second step of the
evaluation. In the second step, we shuffled touch/whisking responses 1,000 times and evaluated
F-values from a one-way ANOVA. If the observed F-value was above the 95th percentile of the
shuffled population distribution of F-values, we deemed the neuron as tuned. This second
evaluation further ensures that the tuning we observed was not due to noise in neural responses.
A neuron was considered significantly location-tuned if it passed both tests. Tuning preference is
the location of the peak response of the tuning curve. To define the width of the tuning, a multiple
comparison test using a Tukey-Kramer-type critical value was used to identify the first bins in both
directions that were significantly different from the peak value. If no bins were significant, no
modulation width was defined. Max and min responses were calculated from BARS-fitted tuning
curves. The absolute modulation depth was calculated as the min response subtracted from the
max response (max-min). The modulation depth was calculated as the min response subtracted
from the max response, divided by the min response added to the max response ((maxmin)/max+min).

32
A Shapiro-Wilk test was used at several steps to assess normality; in most cases,
normality could not be determined, and a non-parametric test was used to test significance.
Neural decoding.
We used multinomial logistic regression to decode pole location implemented using
glmnet (Hastie & Qian, 2016), as described in Cheung et al. 2020. Only touch units that sampled
at least 70% of the pole location range were used for decoding. Each unit had a tuning curve that
was interpolated to 48 bins to estimate location to 0.25-mm resolution. At each bin, 50 samples
were drawn from a Poisson pdf with a λ as the mean of each interpolated bin. We justified drawing
from a Poisson pdf because we found that at touch, the number of spikes generated in the touchresponse window followed a Fano factor of 1.01 ± 0.29 for PTs and 1.03 ± 0.09 for ITs (mean ±
SD). For the design matrix, each row is a location bin, each column is a single neuron, and each
entry is a sampled neural response for the associated neuron.
The decoder was run for 10 iterations. During each iteration, a random 70% of trials were
allocated for training and the remaining 30% for testing. Lasso regularization (alpha parameter
0.95) was used to reduce overfitting. To identify the number of units required, we sampled varying
numbers of neurons with replacement from the units used to train the original model 1000 times.
The indices of the selected neurons were used to create a new population design matrix and
matrix of learned coefficients from the original values. The prediction probabilities of location were
computed using the same method as seen in Cheung et al. 2020. The predicted location was
chosen as the location with the highest probability. Model evaluation of accuracy and resolution
was performed on the test set. Model accuracy was defined as the total number of correct
predictions divided by the total number of predictions. A confusion matrix made from true and
predicted locations was normalized across the total number of given true cases and used to define
the decoding resolution and neurometric curves. Decoding resolution is defined as the total
number of predictions within n bins of the diagonal, where each bin was 0.25 mm.
Supplemental Figures

33
Figure S1. Possible trial outcomes and intrinsic signal imaging. (A) Diagram of possible trial outcomes
(hit, miss, false alarm, correct rejection). (B) Intrinsic signal imaging showing a highlighted region of activity
during piezo-driven whisker stimulation. (C) Skull vasculature with a diagram of the barrel field overlaid and
C2 highlighted in red (bottom).
Figure S2. Opto-tagging results and waveform criteria. (A) Opto-tagging examples for PT (top) and IT
(bottom) cells. Pulse times for 472 nm light are indicated by the cyan ticks above each raw trace. Light was
pulsed at 10 Hz frequency at 10ms pulse width for 1.5s at 9-16 mW. (B) Initial Latency histogram for all
cells retained after an initial 5.6ms time-to-first spike post illumination cut-off was imposed. PTs (N = 36) in
black and ITs (N = 40) in red. Means are displayed as dashed lines of their respective colors.(C) Support
Vector Machine (SVM) - based boundary for further classification of PT and IT cells by their spike waveform
peak-to-trough ratios plotted against their peak-to-trough durations (left). (right) Cells retained in the
population are shown as dots of their respective cell type colors (PTs, black, ITs, red). Open circles indicate
cells for whom we were not confident defining as either one cell type or the other. (D) Latency histogram

34
for all cells retained in the dataset with PTs (N = 32) in black and ITs (N = 26) in red. Means are displayed
as dashed lines of their respective colors (PTs: 2.18 ± 0.87 ms, ITs: 2.41 ± 0.57 ms; mean+sd).
Figure S3. Session Mean Firing Rates. Population mean firing rate for PTs (black, 17.42 ± 3.53 spk/s,
mean ± sd) and ITs (red, 2.23 ± 0.73 spks/s, mean ± sd). The Distribution of the two populations’ responses
was tested with a Two Sample Kolmogorov-Smirnov Test: p = 9.91e-11, k-statistic = 0.88.
Figure S4. Absolute and Relative Angle Modulation vs Phase, Amplitude, and Midpoint. PTs are in
black, and ITs in red, with significantly modulated units filled. All statistical comparisons were done using a
Wilcoxon Signed-Rank Test. (A) (top) Angle absolute modulation plotted against phase absolute

35
modulation, log scale (top). PT: angle geometric mean = 7.19 spks/s, phase geometric mean = 3.50 spks/s,
p = 5.37e-05, z-value = 4.04, signed rank = 480. IT: angle geometric mean = 2.76 spks/s, phase geometric
mean = 0.57 spks/s, p = 1.9e-03, z-value = 3.11, signed rank = 298. (bottom) Angle relative modulation
plotted against phase relative modulation. PT: angle mean modulation = 0.29, phase mean modulation =
0.17, p = 3.59e-05, z-value = 4.13, signed rank = 485. IT: angle mean modulation = 0.47, phase mean
modulation = 0.33, p = 2e-03, z-value = 3.10, signed rank = 297. (B) (top) Angle absolute modulation
plotted against amplitude absolute modulation, log scale (top). PT: angle geometric mean = 7.19 spks/s,
amplitude geometric mean = 4.71 spks/s, p = 1.9e-03, z-value = 3.10, signed rank = 430. IT: angle
geometric mean = 2.76 spks/s, amplitude geometric mean = 1.89 spks/s, p = 0.08, z-value = 1.74, signed
rank = 244. (bottom) Angle relative modulation plotted against amplitude relative modulation. PT: angle
mean modulation = 0.29, amplitude mean modulation = 0.22, p = 4.71e-04, z-value = 3.50, signed rank =
451. IT: angle mean modulation = 0.47, amplitude mean modulation = 0.38, p = 0.02, z-value = 2.35,
signed rank = 268. (C) (top) Angle absolute modulation plotted against midpoint absolute modulation, log
scale (top). PT: angle geometric mean = 7.19 spks/s, midpoint geometric mean = 7.04 spks/s, p = 0.64, zvalue = -0.47, signed rank = 239. IT: angle geometric mean = 2.76 spks/s, midpoint geometric mean =3.69
spks/s, p = 0.07, z-value = -1.84, signed rank = 103. (bottom) Angle relative modulation plotted against
midpoint relative modulation. PT: angle mean modulation = 0.29, midpoint mean modulation = 0.27, p =
0.99, z-value = -0.02, signed rank = 263. IT: angle mean modulation = 0.47, midpoint mean modulation =
0.47, p = 0.56, z-value = -0.57, signed rank = 153.
Figure S5. Percent-increase of firing rate above baseline. (A) Touch-induced above-baseline %
increase of firing rate with PTs (mean increase of 101%) in black and ITs (mean increase of 149%) in red.
Means are displayed as diamonds of each cell type’s respective representative color. Two-Sample
Kolmogorov-Smirnov Test, test statistic = 0.47, p = 0.12. (B) First-touch-induced above-baseline % increase
of firing rate with PTs (mean increase of 341% ) in black and ITs (mean increase of 1361%) in red. Means
are displayed as diamonds of each cell type’s respective representative color. Two-Sample KolmogorovSmirnov Test, test statistic = 0.40, p = 0.25.

36
Figure S6. Touch-count-sorted rasters for significantly touch-responsive cells. Touch-count-sorted
and touch-aligned spike rasters for PT and IT cells, with first touches starting at the bottom of each raster.
PT cell labels are shown in black, IT cell labels are shown in red.

37
Figure S7. ITI touch responses for significantly touch-excited cells. Touch responses plotted against
preceding ITIs for PTs (black cell label) and ITs (red cell label). The x-axis of each subplot starts at the left
from the minimum observed touch ITI for that session and proceeds to the maximum ITI on the right.
Author Contributions
Samson King and Andrew Hires designed the experiments. Samson King conducted all
recordings. Phillip Maire, Adam Mergenthal, and Samson King developed tools for data analysis.
Samson King analyzed the data. Stef Walker assisted with figure design and creation. Samson
King and Andrew Hires wrote the chapter.
Figure S8. Mean adaptation ratios across all touch counts. Mean adaptation ratios for each cell type
(PTs in black, 0.67, ITs in red, 0.57, p = 0.33, k-statistic = 0.38, Two-Sample Kolmogorov-Smirnov Test).

38
Chapter 3 – Conclusions
L5 excitatory cells in S1 receive many local, long-range feedback, and subcortical
inputs. To disambiguate an object’s position in space, it is unknown which suite of these inputs
are indispensable and which cell types are required to interpret and efficiently encode these inputs
to construct a stable object location representation. This gap persists despite great strides taken
from several studies (Cheung et al., 2020; Ranganathan et al., 2018; Xu et al., 2012). In this
thesis, I leveraged two mice line crosses that selectively expressed ChR2(H134R)-eYFP in either
pyramidal tract or intratelencephalic L5 neurons to continue progress toward a concise
understanding as to how touch-mediated object localization is constructed.
In this project, I searched for opto-taggable cells throughout the entire column; this was
an improvement over previous efforts which could not granularly distinguish cell identity. Due to
a unidirectional projection from ITs to PTs, extra care was given to selecting a) short latency cells
and b) cells with dissimilar waveform profiles as determined by a SVM after an initial PCA. This
left me with a dataset of 32 PTs and 26 ITs. The PTs were found to spike reliably in many cases
to touch, to have overall much higher firing rates than ITs, and to have on-average broad tuning
curves to object location that were proficient at predicting pole location when used to train a
multinomial GLM. The ITs had a few members with low-latency touch responses that were very
robust relative to baseline that quickly adapted after first touch. This is quite interesting, as ITs
are usually described as touch-insensitive (Oberlaender et al., 2020), though this may be a
consequence of many whisker-studies that have made recordings with IT cells doing so under
anesthesia. To my knowledge, the rapid adaptive response observed in touch-sensitive IT cells
has also only been described in anesthetized preparations, and then only in passing
(Kheradpezhouh et al., 2017). ITs were also capable of predicting a pole’s true location, but less
well than PTs. Combinations of the two cell types did little to improve performance in decoding
pole position, and decreased performance at lower cell counts.

39
In closing, my project did not provide enough evidence to conclude that PT cells or IT cells
are indispensable in localizing objects in space. However, considering their decoder performance
and their position in the column, PTs are still very likely to be one of the most important cell types
for object localization. ITs, on the other hand, appear able to represent object localization, but
more weakly overall. Their structure and existing known roles in other sensory cortices make it
likely that ITs play a critical role in sharpening stimulus responses of other cells in the local circuit.
Further experiments are necessary in both PTs and ITs to better understand their respective roles
in S1 computations.

40
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Abstract (if available)

Abstract Layer 5 is the canonical output layer of sensory cortex. The two most numerous neural constituents of Layer 5 are pyramidal tract (PT) and intratelencephalic (IT) neurons. These output cell classes combine diverse sets of inputs and project to distinct locations across the brain, suggesting differing roles in sensory information processing. Here, we investigated the representation of touch and whisker motion in these two cell types using optogenetically targeted single unit electrophysiology during whisker-guided object localization. PT neurons (N = 32) had much higher spike rates than IT (N=26) during behavior. Individual members of both were modulated by, but average population firing rates were stable between quiet and whisking periods. PT neurons showed greater absolute spike rate changes, but less relative modulation than IT neurons to whisking kinematic features. Touch-excited PT (N = 18) and IT neurons (N = 8) rapidly adapted to active touch. Both populations encoded the azimuthal position of touched objects, with IT neurons more sharply tuned to position. However, position was more precisely decodable from PT population activity, due to their higher firing rates. A consequence of these characteristics is that PT neurons, with their higher firing rates, may be more effective participants in rate-based neural codes, while IT neurons, with their sharp modulation, may be more effective in timing or synchrony based codes.

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Asset Metadata

Creator King, Samson Garret (author)

Core Title Cell-type specialization of layer 5 excitatory neuron functions in tactile behavior underlying object localization

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Degree Conferral Date 2023-12

Publication Date 12/13/2024

Defense Date 12/07/2023

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

Tag layer 5,mouse,neural circuitry,OAI-PMH Harvest,somatosensory

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor McKemy, David (committee chair), Kalluri, Radha (committee member), (Hires, Samuel Andrew)

Creator Email samsongking@gmail.com,samsonki@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113792442

Unique identifier UC113792442

Identifier etd-KingSamson-12552.pdf (filename)

Legacy Identifier etd-KingSamson-12552

Document Type Dissertation

Format theses (aat)

Rights King, Samson Garret

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Source 20231214-usctheses-batch-1115 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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