Improved dimensionally-reduced visual cortical network using stochastic noise modeling

Authors:
Louis Tao;Jeremy Praissman;Andrew T. Sornborger
Affiliations:
Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetics Engineering, College of Life Sciences, Peking University, Beijing, People's Republic of China 100871 and Ce ...;Department of Biochemistry and Molecular Biology, University of Georgia, Athens, USA 30602;Department of Mathematics and Faculty of Engineering, University of Georgia, Athens, USA 30602
Venue:
Journal of Computational Neuroscience
Year:
2012

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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 of firing rates in the original large-scale simulations while still accurately predicting the orientation preference distribution.