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Related Experiment Video

Updated: Apr 6, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Asynchronous Rate Chaos in Spiking Neuronal Circuits.

Omri Harish¹, David Hansel²

¹Center for Neurophysics, Physiology and Pathologies, CNRS UMR8119 and Institute of Neuroscience and Cognition, Université Paris Descartes, Paris, France.

Plos Computational Biology

|August 1, 2015

Summary

Brain activity can be chaotic, a generic property of neuronal circuits. This study reveals two mechanisms, involving inhibition or excitation-feedback loops, that generate chaotic, asynchronous neural firing.

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Last Updated: Apr 6, 2026

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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

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Published on: September 5, 2012

Area of Science:

Computational neuroscience
Neural dynamics
Complex systems

Background:

The brain exhibits complex, chaotic activity patterns, potentially offering computational advantages.
It remains debated whether neural chaos requires specific cellular or network properties or is a generic feature.

Purpose of the Study:

To investigate the conditions under which chaotic dynamics emerge in excitatory-inhibitory (EI) spiking neural networks.
To identify the underlying mechanisms driving chaotic, asynchronous firing in these networks.

Main Methods:

Utilized Dynamical Mean-Field Theory (DMFT).
Performed numerical simulations of sparse random EI networks.
Analyzed network dynamics under balanced excitation and inhibition.

Main Results:

Chaotic, asynchronous firing rate fluctuations emerge generically in EI networks with strong synapses.
Two distinct mechanisms were identified: slow inhibition and recurrent excitation-inhibition-excitation (E-I-E) feedback loops.
The E-I-E mechanism results in slower firing rates in excitatory populations compared to inhibitory ones.

Conclusions:

Neural chaos is a generic property of balanced EI networks, not requiring specialized cellular or network structures.
The identified mechanisms provide insights into the generation of complex neural dynamics and their potential computational roles.
Findings contribute to understanding the neurophysiological basis of brain function and computation.