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Stabilizing synchrony by inhomogeneity.

Ehsan Bolhasani1,2, Alireza Valizadeh1,2

  • 1Institute for Advanced Studies in Basic Sciences, Department of physics, Zanjan, 45137-66731, Iran.

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Noise disrupts synchrony in neuronal oscillators, but slight differences in neuron firing rates can stabilize synchronization. This finding explains how neuron variability can enhance spike train correlation in connected networks.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Theoretical Neuroscience

Background:

  • Neuronal oscillators exhibit synchronized firing patterns crucial for information processing.
  • Phase resetting curves (PRCs) characterize how neuronal activity is reset by stimuli.
  • Noise and intrinsic parameter mismatch are common factors in biological neural networks.

Purpose of the Study:

  • To investigate the impact of noise and intrinsic parameter mismatch on the synchrony of weakly coupled neuronal oscillators.
  • To determine conditions under which entrainment and phase locking are preserved or disrupted.
  • To elucidate the role of inhomogeneity in stabilizing neuronal synchrony and spike timing precision.

Main Methods:

  • Mathematical modeling of two coupled neuronal oscillators.
  • Analysis of phase resetting curves (PRCs) with strictly positive values.
  • Simulations to observe the effects of additive noise and intrinsic firing rate mismatch.
  • Examination of entrainment, phase slips, and spike train correlation.

Main Results:

  • Isochronous synchrony is lost in the presence of even weak noise, leading to intermittent phase slips.
  • Small mismatches in intrinsic firing rates stabilize phase locking and improve relative spike timing precision.
  • Inhomogeneity enhances neuronal correlation in models like the leaky integrate-fire neuron.

Conclusions:

  • Noise is detrimental to precise synchrony in neuronal networks with positive PRCs.
  • Intrinsic neuronal variability, or inhomogeneity, can paradoxically enhance stable synchronization and precise spike timing.
  • These findings offer insights into how biological neural networks achieve robust and precise information processing despite inherent variability.