Open Collections

Firing Rate Statistics with Intrinsic and Network Heterogeneity

Banff International Research Station for Mathematical Innovation and Discovery

Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. There is still a lot unknown about it; specifically, how 2 sources of heterogeneity: network (synaptic heterogeneity) and intrinsic heterogeneity, interact and alter neural activity is mysterious. In a recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of firing rates. The relationship between intrinsic and network heterogeneity can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically. To analytically characterize our observations, we employ dimension reduction methods and asymptotic analysis to derive compact analytic descriptions of the phenomena. These descriptive formulas show how these 2 forms of heterogeneity determine the firing rate heterogeneity in various settings.

Mathematics; Biology and other natural sciences; Probability theory and stochastic processes; Mathematical biology

video/mp4

Author affiliation: Virginia Commonwealth University

2016-06-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58276

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/