Correlation of Firing in Layered Associative Neural Networks(Condensed Matter : Structure, Mechanical and Thermal Properties)

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概要

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• 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.

• 社団法人日本物理学会の論文
• 2005-08-15

著者

• Okada Masato

  Riken Brain Sci. Inst. Saitama

• Yamana Michiko

  Riken Brain Science Institute Laboratory For Mathematical Neuroscience:(present Address)central Rese

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• Analysis of Ensemble Learning Using Simple Perceptrons Based on Online Learning Theory
• Correlation of Firing in Layered Associative Neural Networks(Condensed Matter : Structure, Mechanical and Thermal Properties)
• Controlling Synfire Chain by Inhibitory Synaptic Input(Cross-disciplinary physics and related areas of science and technology)
• Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model

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