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Slow synaptic dynamics in a network: from exponential to power-law forgetting.

J M Luck1, A Mehta2

  • 1Institut de Physique Théorique, URA 2306 of CNRS, CEA Saclay, 91191 Gif-sur-Yvette Cedex, France.

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Summary

This study models interacting synapses in neural networks, revealing how synaptic competition drives critical dynamics. This competition naturally generates both long- and short-term memory through universal power-law relaxations.

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Area of Science:

  • Computational Neuroscience
  • Complex Systems Theory
  • Synaptic Plasticity

Background:

  • Neural networks exhibit complex dynamics driven by synaptic interactions.
  • Understanding synaptic adaptation is crucial for deciphering memory formation.

Purpose of the Study:

  • To investigate a mean-field model of interacting synapses on a directed neural network.
  • To explore the role of synaptic competition in adaptive dynamics and memory emergence.

Main Methods:

  • Developed a mean-field model incorporating Hebbian cooperation and a novel polarity-driven synaptic competition rule.
  • Analyzed the model's dynamics to identify critical manifolds and relaxation behaviors.

Main Results:

  • Synaptic competition is essential for the emergence of a critical manifold and a tricritical point.
  • Universal 1/t and 1/√[t] power-law relaxations of mean synaptic strength were observed.
  • Demonstrated the natural emergence of long- and short-term memory from different parameter regimes.

Conclusions:

  • Synaptic competition plays a pivotal role in shaping neural network dynamics and memory.
  • The model provides a theoretical framework for understanding universal relaxation phenomena in synaptic plasticity.
  • Highlights the potential for emergent memory functions in biological and artificial neural systems.