Improved dimensionally-reduced visual cortical network using stochastic noise modeling

Louis Tao, Jeremy Praissman, Andrew T. Sornborger

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)367-376
Number of pages10
JournalJournal of Computational Neuroscience
Volume32
Issue number2
DOIs
StatePublished - Apr 2012
Externally publishedYes

Keywords

  • Autoregressive process
  • Low dimensional characterization
  • Primary visual cortex
  • Stochastic process

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