Multiplicative Shot-Noise: A New Route to Stability of Plastic Networks

Bin Wang and Johnatan Aljadeff
Phys. Rev. Lett. 129, 068101 – Published 2 August 2022
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Abstract

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events of two “parent” processes. We propose a multiplicative shot-noise model that can capture the behaviors of a broad range of such natural phenomena, and analytically derive an approximation that accurately predicts its statistics. We apply our results to study the effects of a multiplicative synaptic plasticity rule that was recently extracted from measurements in physiological conditions. Using mean-field theory analysis and network simulations, we investigate how this rule shapes the connectivity and dynamics of recurrent spiking neural networks. The multiplicative plasticity rule is shown to support efficient learning of input stimuli, and it gives a stable, unimodal synaptic-weight distribution with a large fraction of strong synapses. The strong synapses remain stable over long times but do not “run away.” Our results suggest that the multiplicative shot-noise offers a new route to understand the tradeoff between flexibility and stability in neural circuits and other dynamic networks.

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  • Received 21 January 2022
  • Accepted 30 June 2022

DOI:https://doi.org/10.1103/PhysRevLett.129.068101

© 2022 American Physical Society

Physics Subject Headings (PhySH)

Interdisciplinary PhysicsNetworksNonlinear DynamicsStatistical Physics & Thermodynamics

Authors & Affiliations

Bin Wang

  • Department of Physics, University of California San Diego, La Jolla, California 92093, USA

Johnatan Aljadeff*

  • Department of Neurobiology, University of California San Diego, La Jolla, California 92093, USA

  • *Corresponding author. aljadeff@ucsd.edu

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Issue

Vol. 129, Iss. 6 — 5 August 2022

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