CHAOTIC IMAGE RETRIEVAL IN MARKOVIAN ASYMMETRIC NEURAL NETWORKS WITH SIGN-CONSTRAINED SYNAPTIC COUPLINGS

We propose a simple asymmetric neural network which exhibits chaotic motions in retrieval dynamics with a finite number of memory patterns. The characteristic feature of the model is that the synaptic couplings are designed in such a way that each neuron is given an exclusively excitatory or inhibitory function, i.e., a physiological constraint of the Dale hypothesis is taken into account. The updating rule of the neurons is assumed to be simple Markovian stochastic dynamics of the Little type (without time delay) in which the threshold for neuron firing is incorporated. Our analysis is based on the exact time evolution equations derived in the thermodynamic limit for the macroscopic pattern overlaps. It is shown that chaotic image retrieval can take place only when a finite amount of stochastic noise exists. © 1990, The Physical Society of Japan. All rights reserved.