Mechanisms of self-sustained oscillatory states in hierarchical modular networks with mixtures of electrophysiological cell types

Petar Tomov, Rodrigo F.O. Pena, Antonio C. Roque, Michael A. Zaks

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics “up” and “down” states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons. In particular, we observe that the crucial role both in interruption and in resumption of global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is more influenced by their presynaptic environment in the network than by their formal types, assigned in accordance with their response to constant current.

Original languageEnglish (US)
Article number23
JournalFrontiers in Computational Neuroscience
Volume10
Issue numberMAR
DOIs
StatePublished - Mar 23 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

Keywords

  • Chaotic neural dynamics
  • Cortical network models
  • Cortical oscillations
  • Hierarchical modular networks
  • Intrinsic neuronal diversity
  • Irregular firing activity
  • Self-sustained activity
  • Up-down states

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