SELF-ORGANIZED PHASE-TRANSITIONS IN NEURAL NETWORKS AS A NEURAL MECHANISM OF INFORMATION-PROCESSING

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of informatio n processing in a population coding scheme is transition between the a ctivity patterns encoding usual entities. We propose that the infinite simal and short-term synaptic changes based on the Hebbian learning ru le are the driving force for the transition, The phase transition betw een the following two dynamical stable states is studied in detail, th e state where the firing pattern is changed temporally so as to itiner ate among several patterns and the state where the firing pattern is f ixed to one of several patterns, The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbia n learning process under a weak input relevant to the fixed pattern, T he reverse transition mag be induced by the Hebbian unlearning process without input, The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input, To ensure that information processing based on the phase transition can be made by the infinites imal and short-term synaptic changes, it is absolutely necessary that the network always stags near the critical state corresponding to the phase transition point.