Abstract
A class of recurrent neural networks is considered in which w_<ij>the connection weight from the jth to the ith element, is randomly generated under the condition wij=wji. The expected number of equilibrium states in the network consisting of two-state threshold elements having outputs of(-1, 1)or(0, 1)with variable thresholds, but uniform throughout the network, is derived by the method of statistical neurodynamics. It is shown that the expected number of equilibrium states and the rate of excited neurons in these states are uniquely determined by the threshold value. Applications of this network as a module for memory systems are discussed.
