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Related Experiment Videos

Doubly stochastic coherence in complex neuronal networks.

Yang Gao1, Jianjun Wang

  • 1College of Nuclear Science and Technology, Harbin Engineering University, Harbin, China. gaoyang@hrbeu.edu.cn

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

Optimal noise levels enhance neuronal network spiking regularity, achieving doubly stochastic coherence. Network topology impacts this coherence, with randomness generally hindering spiking regularity, especially at higher coupling strengths.

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Area of Science:

  • Computational Neuroscience
  • Complex Systems

Background:

  • Neuronal networks exhibit complex dynamics influenced by noise and topology.
  • Understanding spiking regularity is crucial for comprehending neural information processing.

Purpose of the Study:

  • To investigate the impact of coupled FitzHugh-Nagumo neurons with varying topologies under dual noise conditions.
  • To analyze how network topology and noise intensity affect neuronal spiking regularity and temporal coherence.

Main Methods:

  • Simulated coupled FitzHugh-Nagumo neurons with different network topologies (regular, small-world, random).
  • Introduced independent additive and multiplicative Gaussian white noises.
  • Varied noise intensities and coupling strengths to observe effects on spiking regularity.

Main Results:

  • Doubly stochastic coherence achieved at optimal additive and multiplicative noise intensities, maximizing temporal periodicity.
  • Network topology randomness had minimal effect on spiking regularity at low coupling strengths.
  • Small-world networks showed slight improvement in temporal periodicity with long-range connections at intermediate coupling.
  • At high coupling strengths, resonance properties were lost, and randomness decreased spiking regularity.

Conclusions:

  • Optimal noise levels can induce doubly stochastic coherence in neuronal networks.
  • Network topology randomness generally depresses temporal coherence of spiking oscillations, particularly at higher coupling strengths.
  • The interplay between noise, coupling strength, and network topology critically determines neuronal network dynamics and regularity.