Firing Rate Statistics with Intrinsic and Network Heterogeneity

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.Non UBCUnreviewedAuthor affiliation: Virginia Commonwealth UniversityFacult