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

概要

論文の詳細を見る
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.
日本応用数理学会の論文
1997-06-15