TY - JOUR
T1 - Improved dimensionally-reduced visual cortical network using stochastic noise modeling
AU - Tao, Louis
AU - Praissman, Jeremy
AU - Sornborger, Andrew T.
N1 - Funding Information:
This work was supported by NIH NIBIB 005432 (ATS), NIH NINDS 070159 (ATS) and the National Basic Research Program of China (973 Program 2011CB809105) (LT).
PY - 2012/4
Y1 - 2012/4
N2 - 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.
AB - 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.
KW - Autoregressive process
KW - Low dimensional characterization
KW - Primary visual cortex
KW - Stochastic process
UR - http://www.scopus.com/inward/record.url?scp=84863878722&partnerID=8YFLogxK
U2 - 10.1007/s10827-011-0359-3
DO - 10.1007/s10827-011-0359-3
M3 - Article
C2 - 21874340
AN - SCOPUS:84863878722
SN - 0929-5313
VL - 32
SP - 367
EP - 376
JO - Journal of Computational Neuroscience
JF - Journal of Computational Neuroscience
IS - 2
ER -