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Spiking regularity in a noisy small-world neuronal network.

Qianshu Li1, Yang Gao

  • 1The State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing, 100081, China. qsli@bit.edu.cn

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Area of Science:

  • Computational Neuroscience
  • Complex Systems

Background:

  • Neuronal oscillations are fundamental to brain function.
  • Network topology significantly influences neural signal processing.
  • Understanding how noise affects neural synchrony is crucial.

Purpose of the Study:

  • To investigate the impact of network topology on spiking oscillation regularity.
  • To explore the phenomenon of coherence resonance in neuronal networks.
  • To identify optimal conditions for enhancing spike train regularity.

Main Methods:

  • Simulating coupled Fitz-Hugh-Nagumo neurons.
  • Employing Watts-Strogatz small-world and regular network structures.
  • Analyzing spike train regularity under varying colored noise intensities and correlation times.

Main Results:

  • Coherence resonance observed in both regular and small-world networks at moderate noise intensities.
  • Small-world networks demonstrate superior temporal coherence compared to regular networks.
  • Spiking regularity is tunable by network topology randomness and noise correlation time.

Conclusions:

  • Network topology, particularly small-world structures, enhances neuronal signal regularity.
  • Coherence resonance and noise correlation are key factors in optimizing neural signal processing.
  • Small-world networks offer robustness to local noise through optimized signal coherence.