Practical approximation method for firing-rate models of coupled neural networks with correlated inputs

Andrea K. Barreiro and Cheng Ly
Phys. Rev. E 96, 022413 – Published 23 August 2017

Abstract

Rapid experimental advances now enable simultaneous electrophysiological recording of neural activity at single-cell resolution across large regions of the nervous system. Models of this neural network activity will necessarily increase in size and complexity, thus increasing the computational cost of simulating them and the challenge of analyzing them. Here we present a method to approximate the activity and firing statistics of a general firing rate network model (of the Wilson-Cowan type) subject to noisy correlated background inputs. The method requires solving a system of transcendental equations and is fast compared to Monte Carlo simulations of coupled stochastic differential equations. We implement the method with several examples of coupled neural networks and show that the results are quantitatively accurate even with moderate coupling strengths and an appreciable amount of heterogeneity in many parameters. This work should be useful for investigating how various neural attributes qualitatively affect the spiking statistics of coupled neural networks.

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  • Received 13 February 2017

DOI:https://doi.org/10.1103/PhysRevE.96.022413

©2017 American Physical Society

Physics Subject Headings (PhySH)

Physics of Living Systems

Authors & Affiliations

Andrea K. Barreiro

  • Department of Mathematics, Southern Methodist University, P.O. Box 750235, Dallas, Texas 75275, USA

Cheng Ly*

  • Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, 1015 Floyd Avenue, Richmond, Virginia 23284, USA

  • *CLy@vcu.edu

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Vol. 96, Iss. 2 — August 2017

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