Neural activities and cluster-formation in a random neural network
Nobuyuki Matsui, Eiichi Bamba · 1991
An approach to a macroscopic description of a cluster-formation algorithm by neural activities in a random neural network is considered. The activity interaction between clusters of neurons and the network entropy through the medium of the activity parameter x(p) for the input pattern p, are introduced as a system energy. By using the neural state transition rule similar to that in the Boltzmann network and some simple stochastic assumptions, cluster-formation of neurons was simulated. The relations between cluster sizes, or the simulated activity, and the setting activity parameter are shown. The validity of this macroscopic description is also discussed.>