Abstract/Details

Modeling and characterization of neural gain control


2002 2002

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Abstract (summary)

Sensory neurons often exhibit striking nonlinear behaviors that are not adequately described by a linear receptive field representation.

In the first part of the work, we suggest that these nonlinearities arise because sensory systems are designed to efficiently represent environmental information. We describe a form of nonlinear decomposition (specifically, divisive gain control) that is well-suited for efficient encoding of natural signals. We show that this decomposition, with parameters optimized for the statistics of a generic ensemble of natural images or sounds, can account for some nonlinear response properties of “typical” neurons in both vision (area V1) and audition (auditory nerve). This work provides theoretical justification to neural models of gain control, and explains how one might choose the parameters of the model based on efficient coding considerations.

In the second part of the work, we describe a methodology for characterizing this class of nonlinear sensory models. The characterization is based on a white noise analysis, in which a set of random stimuli are presented to a neuron and the spike-triggered ensemble (specifically, the spike-triggered covariance) is analyzed. We demonstrate the applicability of the technique to retinal ganglion cell data in monkey and salamander.

Indexing (details)


Subject
Neurology
Classification
0317: Neurology
Identifier / keyword
Biological sciences; Computational neuroscience; Neural gain control; Vision
Title
Modeling and characterization of neural gain control
Author
Schwartz, Odelia
Number of pages
200
Publication year
2002
Degree date
2002
School code
0146
Source
DAI-B 63/08, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780493809601, 0493809600
Advisor
Simoncelli, Eero P.
University/institution
New York University
University location
United States -- New York
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3062840
ProQuest document ID
251115406
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/251115406
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