A robust balancing mechanism for spiking neural networks

Article Properties
  • Language
    English
  • DOI (url)
  • Publication Date
    2024/04/01
  • Indian UGC (Journal)
  • Refrences
    71
  • Antonio Politi Institute for Complex Systems and Mathematical Biology and Department of Physics 1 , Aberdeen AB24 3UE, United KingdomCNR—Consiglio Nazionale delle Ricerche—Istituto dei Sistemi Complessi 2 , via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy ORCID (unauthenticated)
  • Alessandro Torcini CNR—Consiglio Nazionale delle Ricerche—Istituto dei Sistemi Complessi 2 , via Madonna del Piano 10, 50019 Sesto Fiorentino, ItalyLaboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université 3 , CNRS UMR 8089, 95302 Cergy-Pontoise cedex, FranceINFN Sezione di Firenze 4 , Via Sansone 1 50019 Sesto Fiorentino, Italy ORCID (unauthenticated)
Abstract
Cite
Politi, Antonio, and Alessandro Torcini. “A Robust Balancing Mechanism for Spiking Neural Networks”. Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 34, no. 4, 2024, https://doi.org/10.1063/5.0199298.
Politi, A., & Torcini, A. (2024). A robust balancing mechanism for spiking neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(4). https://doi.org/10.1063/5.0199298
Politi A, Torcini A. A robust balancing mechanism for spiking neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2024;34(4).
Journal Categories
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Mathematics
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Description

How do our brains maintain stability amidst constant neural activity? This research proposes a novel, robust nonlinear balancing mechanism for random networks of spiking neurons. This mechanism explains the irregular, low-firing activity observed in the cortex, even without strong external currents. The proposed mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. The nonlinear response of synaptic activity is identified as a key factor in establishing a stable, balanced regime. The researchers support their claims with a self-consistent analysis and extensive simulations across increasing network sizes. The resulting regime is fluctuation-driven, characterized by highly irregular spiking dynamics across all neurons. This study offers valuable insights into the complex dynamics underlying brain function, providing a new perspective on neural balancing.

This study, published in Chaos: An Interdisciplinary Journal of Nonlinear Science, aligns with the journal's focus on complex systems and nonlinear dynamics. By proposing a novel mechanism for neural balancing in spiking neural networks, the paper contributes to a deeper understanding of complex dynamics within the brain.

Refrences
Refrences Analysis
The category Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry 59 is the most frequently represented among the references in this article. It primarily includes studies from Physical Review Letters The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year