A robust balancing mechanism for spiking neural networks

Antonio Politi; Alessandro Torcini

doi:10.48550/arXiv.2401.12559

A robust balancing mechanism for spiking neural networks

Antonio Politi, Alessandro Torcini^* (Corresponding Author)

^*Corresponding author for this work

Physics

Research output: Contribution to journal › Article › peer-review

Abstract

Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.

Original language	English
Number of pages	8
Journal	Chaos
Volume	34
Issue number	4
Early online date	19 Apr 2024
DOIs	https://doi.org/10.48550/arXiv.2401.12559 https://doi.org/10.1063/5.0199298
Publication status	Published - 19 Apr 2024

Bibliographical note

ACKNOWLEDGMENTS
We thank German Mato for useful discussions in the initial stage of this project, during our stay at Max Planck Institute for the Physics of Complex Systems (Dresden, Germany) within the Advanced Study Group “From Microscopic to Collective Dynamics in Neural Circuits” (2016/17). A.T. also acknowledges useful interactions with Gianluigi Mongillo and Nina La Miciotta. A.T. received financial support by the ANR Project ERMUNDY (Grant No. ANR-18-CE37-0014), by the Labex MME-DII (No. ANR-11-LBX-0023-01) and by CY Generations (Grant No. ANR-21-EXES-0008), all part of the French program “Investissements d’Avenir.” A.P. received support by CY Advanced Studies (Cergy-Pontoise, France) for a visiting scholarship in 2018.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Keywords

Action Potentials/physiology
Models, Neurological
Neural Networks, Computer
Neurons/physiology
Synapses/physiology
Neuronal Plasticity/physiology

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10.1063/5.0199298Licence: Unspecified

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Cite this

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title = "A robust balancing mechanism for spiking neural networks",

abstract = "Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.",

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KW - Neural Networks, Computer

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KW - Synapses/physiology

KW - Neuronal Plasticity/physiology

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A robust balancing mechanism for spiking neural networks

Abstract

Bibliographical note

Data Availability Statement

Keywords

Access to Document

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