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Information space dynamics for neural networks.

R M C de Almeida1, M A P Idiart

  • 1Instituto de Física, Universidade Federal do Rio Grande do Sul, Caixa Postal 15051, 91501-970 Porto Alegre, RS, Brazil.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 22, 2002
PubMed
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This study introduces a novel neural network model for memory retrieval using a coupled map lattice. The model demonstrates associative memory capabilities and exhibits recency and latency effects in information processing.

Area of Science:

  • Computational Neuroscience
  • Complex Systems

Background:

  • Neural networks are crucial for memory retrieval.
  • Modeling information processing in neural networks requires understanding neuronal activity and spiking phases.

Purpose of the Study:

  • To propose a coupled map lattice model for neural network memory retrieval.
  • To investigate the model's associative memory function and information processing characteristics.

Main Methods:

  • A coupled map lattice on an M-dimensional hypercube was developed.
  • Logistic maps were used to model the evolution of network state intensity.
  • Hebbian learning rules were derived and applied.

Main Results:

  • The model functions as an associative memory.

Related Experiment Videos

  • Network capacity and basin of attraction sizes were numerically investigated.
  • Recency and latency effects were observed in response to stimuli, influenced by noise and delay.

    • Conclusions:

    • The proposed model offers a framework for understanding memory retrieval in neural networks.
    • The model captures key phenomena like associative recall and stimulus-response dynamics.