Correlation of Firing in Layered Associative Neural Networks

DOI Web Site Web Site BIBCODE 5 Citations 34 References
  • Yamana Michiko
    RIKEN, Brain Science Institute, Laboratory for Mathematical Neuroscience
  • Okada Masato
    RIKEN, Brain Science Institute, Laboratory for Mathematical Neuroscience Graduate School of Frontier Sciences, The University of Tokyo “Intelligent Cooperation and Control”, PRESTO, JST

Search this article

Abstract

There is growing interest in a phenomenon called the “synfire chain”, in which firings of neurons propagate from pool to pool in the chain. The mechanism of the synfire chain has been analyzed by many researchers. Keeping the synfire chain phenomenon in mind, we investigate a layered associative memory neural network model, in which patterns are embedded in connections between neurons. In this model, we also include uniform noise in connections, which induces common input in the next layer. Such common input in layers generate correlated firings of neurons. We theoretically obtain the evolution of retrieval states in the case of infinite pattern loading. We find a break down of self-averaging property, that is, the overlap between patterns and neuronal states is not given as a deterministic quantity, but is described by a probability distribution defined over the ensemble of synaptic matrices. Our simulation results are in excellent agreement with theoretical calculations.

Journal

Citations (5)*help

See more

References(34)*help

See more

Keywords

Details 詳細情報について

Report a problem

Back to top