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NONLINEAR MODELING OF CAUSAL INTERRELATIONSHIPS IN NEURONAL
ENSEMBLES:
AN APPLICATION TO THE RAT HIPPOCAMPUS
by
Theodoros Zanos
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOMEDICAL ENGINEERING)
May 2009
Copyright 2009 Theodoros Zanos
Object Description
| Title | Nonlinear modeling of causal interrelationships in neuronal ensembles: an application to the rat hippocampus |
| Author | Zanos, Theodoros |
| Author email | zanos@usc.edu; theozanos@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biomedical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-10-31 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-05-11 |
| Advisor (committee chair) | Marmarelis, Vasilis Z. |
| Advisor (committee member) |
Berger, Theodore W. D'Argenio, David Courellis, Spyros Schaal, Stefan |
| Abstract | The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of "multidimensional" time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials -- treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. Nonparametric, data driven models with predictive capabilities are excellent candidates for these purposes. When modeling input-output relations in multi-input neuronal systems, it is important to select the subset of inputs that are functionally and causally related to the output. Inputs that do not convey information about the actual transformation not only increase the computational burden but also affect the generalization of the model. Moreover, a reliable functional connectivity measure can provide patterns of information flow that can be linked to physiological and anatomical properties of the system. This Doctoral Thesis presents a multiple-input/multiple-output (MIMO) modeling methodology that can be used to quantify the neuronal dynamics of causal functional relationships in neuronal ensembles using spike-train data recorded from individual neurons. Part of this methodological framework is a novel functional connectivity algorithm. Results from simulated systems and for actual applications using multiunit data recordings from the hippocampus of behaving rats are presented. |
| Keyword | computational neuroscience; system identification; volterra; nonlinear; modeling; functional connectivity; point process; hippocampus; CA3; CA1 |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2215 |
| Rights | Zanos, Theodoros |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Zanos-2727 |
| Archival file | uscthesesreloadpub_Volume17/etd-Zanos-2727.pdf |
Description
| Title | Page 1 |
| Full text | NONLINEAR MODELING OF CAUSAL INTERRELATIONSHIPS IN NEURONAL ENSEMBLES: AN APPLICATION TO THE RAT HIPPOCAMPUS by Theodoros Zanos A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOMEDICAL ENGINEERING) May 2009 Copyright 2009 Theodoros Zanos |
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