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

Jiwei Zhang, Katherine Newhall, Douglas Zhou, Aaditya Rangan

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)279-295
Number of pages17
JournalJournal of Computational Neuroscience
Volume36
Issue number2
DOIs
StatePublished - Apr 2014

Keywords

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

ASJC Scopus subject areas

  • Sensory Systems
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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