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Dynamic neural network for visual memory

A dynamic neural network with neural computing units that exhibit hysteresis phenomena is proposed as a mechanism for visual memory. The neural network, named the PN-processor, is loosely based on a mathematical theory proposed by Wilson and Cowan to describe the functional dynamics of conical nervous tissue. The individual neural computing units of the network are programmed to exhibit localized hysteresis phenomena. This neural network structure is capable of storing visual information without physical changes to its synaptic connections. External stimuli move the network's neural activity around a high-dimensional phase space of state attractors until the overall response is stabilized. Once stabilized, the response remains unperturbed by weak or familiar stimuli and is changed only by a sufficiently strong new input. In this paper we briefly describe several aspects of this type of visual memory.