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Related Experiment Videos

Storage capacity diverges with synaptic efficiency in an associative memory model with synaptic delay and pruning.

Seiji Miyoshi1, Masato Okada

  • 1Graduate School of Frontier Science, University of Tokyo, Tokyo, Japan.

IEEE Transactions on Neural Networks
|October 16, 2004
PubMed
Summary

Introducing delayed synapses and pruning in associative memory models enhances storage capacity. This approach overcomes limitations of traditional synaptic pruning, suggesting the brain may favor dynamic memory storage.

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Area of Science:

  • Computational Neuroscience
  • Network Science
  • Memory Models

Background:

  • Synaptic pruning increases storage capacity per synapse in associative memory models, but decreases overall network capacity.
  • Existing models face limitations in balancing synaptic efficiency and network-level storage.

Purpose of the Study:

  • To propose a novel associative memory model that enhances storage capacity by introducing delayed synapses and pruning.
  • To investigate the impact of decreasing connectivity while maintaining a constant total number of synapses.

Main Methods:

  • Utilized statistical neurodynamics to explain the Yanai-Kim theory for networks with serial delay elements.
  • Employed discrete Fourier transformation to rederive macroscopic steady-state equations, considering translational symmetry.

Related Experiment Videos

  • Analyzed storage capacities quantitatively for discrete synchronous-type models with delayed synapses and pruning.

    • Main Results:

    • Storage capacity increases with delay length and decreased connectivity when the total number of synapses remains constant.
    • Random pruning leads to storage capacity asymptotically approaching 2/pi.
    • Systematic pruning results in storage capacity diverging logarithmically with delay length (proportion constant 4/pi).

    Conclusions:

    • The proposed model theoretically supports the significance of synaptic pruning after overgrowth in the brain.
    • Findings suggest a potential preference in the brain for storing dynamic attractors (sequences, limit cycles) over equilibrium states.