Properties of Neuronal Model Dependence in the Randomly and Symmetrically Connected Neural Networks

DOI Web Site 14 References
  • Date Akira
    Department of Biophysical Engineering Faculty of Engineering Science, Osaka University

Bibliographic Information

Other Title
  • ランダム対称結合神経回路網の神経細胞モデル依存特性
  • ランダム タイショウ ケツゴウ シンケイ カイロモウ ノ シンケイ サイボウ

Search this article

Abstract

A large number of equilibrium states or fixed points is in a randomly and symmetrically connected neural network. Recently it has been shown that the maximum number which can be realized depend on the model of the single neuron. Here we show some network properites of the neuronal model dependence which include the maximum number of equibrium states and the activity of these states. Furthermore, the invariant activity in each model is also derived, where the activity does not depend on the statistical parameters designated by the probability distribution of connection weights between neurons and a threshold of neurons.

Journal

References(14)*help

See more

Details 詳細情報について

Report a problem

Back to top