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Correction: Bourahmah et al. Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks. <i>Brain Sci.</i> 2024, <i>14</i>, 468.

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Robust design of polyrhythmic neural circuits.

Justus T C Schwabedal1, Alexander B Neiman2, Andrey L Shilnikov3

  • 1Neuroscience Institute, Georgia State University, Atlanta, Georgia 30303, USA.

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Neuronal networks can exhibit multiple rhythms, but noise can disrupt them. Balancing network parameters enhances rhythm stability against this noise, crucial for reliable neural function.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Theoretical Neuroscience

Background:

  • Neural circuit motifs are fundamental building blocks for complex neuronal functions.
  • These motifs can generate multiple coexisting rhythmic patterns (polyrhythms).
  • Understanding the robustness of these rhythms to perturbations is critical for network reliability.

Purpose of the Study:

  • To investigate the robustness of a neural circuit motif sustaining bursting polyrhythms under random perturbations.
  • To analyze the impact of synaptic coupling strength and noise on rhythm stability.
  • To develop a mechanistic understanding of noise-induced rhythm switching and identify strategies for enhancing robustness.

Main Methods:

  • Modeling inhibitory neuronal networks with coexisting rhythmic patterns.
  • Analyzing the effects of synaptic coupling strength on rhythm stability in the absence and presence of noise.
  • Developing a generic description of the dynamic mechanism underlying stochastic arrhythmia.

Main Results:

  • Increased synaptic coupling enhances rhythm robustness without noise.
  • Beyond a critical coupling strength, increased noise leads to intensified rhythm switching, decreasing robustness.
  • A mechanistic insight into stochastic arrhythmia was developed.

Conclusions:

  • The robustness of polyrhythms in neuronal networks is sensitive to the interplay between synaptic coupling and noise.
  • Balancing physiological parameters of neuronal dynamics and network coupling is essential for enhancing rhythm robustness against noise.
  • Findings are applicable to various relaxation-oscillator networks, including Fitzhugh-Nagumo and Hodgkin-Huxley models.