Neural activities and cluster-formation in a random neural network

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