Improved dimensionally-reduced visual cortical network using stochastic noise modeling

Document Type

Article

Publication Date

1-1-2012

Abstract

In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework includes modeling the effect of neglected modes as a stochastic process. By parametrizing and including stochasticity in one of two ways we show that we can improve the systems-level characterization of our dimensionally reduced neuronal network model. We examined orientation selectivity maps calculated from the firing rate distribution of large-scale simulations and stochastic dimensionally reduced models and found that by using stochastic processes to model the neglected modes, we were able to better reproduce the mean and variance offiring rates in the original large-scale simulations while still accurately predicting the orientation preference distribution. © Springer Science+Business Media, LLC 2011.

Identifier

84863878722 (Scopus)

Publication Title

Journal of Computational Neuroscience

External Full Text Location

https://doi.org/10.1007/s10827-011-0359-3

e-ISSN

15736873

ISSN

09295313

PubMed ID

21874340

First Page

367

Last Page

376

Issue

2

Volume

32

Grant

005432

Fund Ref

National Institute of Neurological Disorders and Stroke

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