REPRESENTATION AND PROCESSING IN A STOCHASTIC NEURAL NETWORK - AN INTEGRATED APPROACH

To relate the stochastic properties of individual neurons to the representational and processing abilities of a network built with these neurons, a generic framework is introduced. Herein, representational abilities become expressed in terms of the type of probability density function the network uses to encode the environment from which it receives input. Processing abilities are characterized by the type of processing procedure used by the network, an analogue of simulated annealing, and are shown to be ultimately related to representational abilities. The condition under which the analogue converges is established.