Journal of Computational Neuroscience

, Volume 36, Issue 2, pp 279-295

First online:

Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks

  • Jiwei ZhangAffiliated withCourant Institute of Mathematical Sciences, New York University
  • , Katherine NewhallAffiliated withCourant Institute of Mathematical Sciences, New York University Email author 
  • , Douglas ZhouAffiliated withDepartment of Mathematics, MOE-LSC and Institute of Natural Sciences, Shanghai Jiao Tong University
  • , Aaditya RanganAffiliated withCourant Institute of Mathematical Sciences, New York University

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Randomly connected populations of spiking neurons display a rich variety of dynamics. However, much of the current modeling and theoretical work has focused on two dynamical extremes: on one hand homogeneous dynamics characterized by weak correlations between neurons, and on the other hand total synchrony characterized by large populations firing in unison. In this paper we address the conceptual issue of how to mathematically characterize the partially synchronous “multiple firing events” (MFEs) which manifest in between these two dynamical extremes. We further develop a geometric method for obtaining the distribution of magnitudes of these MFEs by recasting the cascading firing event process as a first-passage time problem, and deriving an analytical approximation of the first passage time density valid for large neuron populations. Thus, we establish a direct link between the voltage distributions of excitatory and inhibitory neurons and the number of neurons firing in an MFE that can be easily integrated into population–based computational methods, thereby bridging the gap between homogeneous firing regimes and total synchrony.


Spiking neurons Synchrony Homogeneity Multiple firing events First passage time Integrate and fire neuronal networks