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Related Experiment Videos

Memory capacity of balanced networks.

Yuval Aviel1, David Horn, Moshe Abeles

  • 1Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel. yuval@alum.cs.huji.ac.il

Neural Computation
|April 2, 2005
PubMed
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This study explores memory capacity in spiking neural networks using synfire chains (SFC) and Hebbian cell assemblies (HCA). Introducing inhibitory assemblies enhances capacity in these balanced neural networks.

Area of Science:

  • Computational Neuroscience
  • Neural Networks
  • Memory Systems

Background:

  • Balanced networks of spiking neurons are crucial for understanding neural computation.
  • Associative memories can be implemented using synfire chains (SFC) or Hebbian cell assemblies (HCA).
  • Dynamical constraints limit the size of neuronal pools in these memory models.

Purpose of the Study:

  • To investigate the memory capacity of balanced spiking neural networks.
  • To determine the upper limits on memory capacity imposed by network dynamics.
  • To explore methods for improving memory capacity through network architecture modifications.

Main Methods:

  • Embedding SFC and HCA models within balanced spiking neural networks.
  • Utilizing combinatorial arguments to derive theoretical upper bounds on memory capacity.

Related Experiment Videos

  • Performing simulations to measure the dynamic capacity (alpha(c)) for HCA and SFC.
  • Introducing inhibitory cell assemblies ('shadow patterns') to create doubly balanced networks.

    • Main Results:

    • Hebbian cell assemblies (HCA) show a dynamic capacity (alpha(c)) of approximately 0.1.
    • Synfire chains (SFC) exhibit a dynamic capacity (alpha(c)) of approximately 0.065.
    • The introduction of inhibitory assemblies significantly improves memory capacity in both models.
    • Identified viable operating regions (phase space) for network parameters (w(E)/sqrt(K)).

    Conclusions:

    • Memory capacity in balanced spiking neural networks is constrained by network dynamics.
    • Doubly balanced networks, incorporating inhibitory assemblies, offer enhanced memory storage.
    • The findings provide a framework for designing neural network architectures with improved memory functions.