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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

How Chaotic is the Balanced State?

Sven Jahnke1, Raoul-Martin Memmesheimer, Marc Timme

  • 1Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization Göttingen, Germany. sjahnke@nld.ds.mpg.de

Frontiers in Computational Neuroscience
|November 26, 2009
PubMed
Summary
This summary is machine-generated.

Spiking neural networks show irregular activity. This study finds that while slow synaptic responses or too many excitatory interactions can lead to chaos, stable dynamics also generate this irregularity, suggesting chaos isn't essential for neural activity.

Keywords:
attractor neural networksbalanced stateirregular activitylocal cortical circuitsstabilitysynchronization

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Area of Science:

  • Computational neuroscience
  • Theoretical neuroscience
  • Neural dynamics

Background:

  • Spiking neural networks often display a balanced state characterized by highly irregular activity.
  • This irregular activity is observed across various network configurations, including those with external excitatory inputs and recurrent inhibition, as well as mixed excitation-inhibition.

Purpose of the Study:

  • To analytically investigate the irregular dynamics in finite spiking neural networks.
  • To determine the stability of these dynamics and identify conditions leading to chaos or stable periodic orbits.

Main Methods:

  • Analytical investigation of finite spiking neural networks.
  • Tracking individual spike times and neuron identities.
  • Analysis of dynamics under varying synaptic response timescales and ratios of inhibitory to excitatory interactions.

Main Results:

  • Purely inhibitory networks with delayed interactions exhibit stable, non-chaotic irregular dynamics that converge to stable periodic orbits.
  • Increasing synaptic response time scales or the proportion of excitatory interactions can lead to a smooth transition to chaos.
  • Both chaotic and stable dynamics can generate irregular neuronal activity.

Conclusions:

  • Chaos is not essential for generating the high irregularity observed in balanced neural activity.
  • A mechanism distinct from chaos and stochasticity likely contributes significantly to irregular activity in cortical circuits.
  • Stable dynamics play a crucial role in producing irregular neuronal firing patterns.