A computational model of learning and memory

Learning is the mapping of outer environmental information onto synaptic weight space. Hebb proposed a learning rule based on the AND operation between the input and the output neuron, which became the foundation for future learning rules. We previously proposed a spatio-temporal learning rule based on differences observed in hippocampal long-term potentiation (LTP) induced by various spatio-temporal pattern stimuli. This rule was applied to learn spatio-temporal patterns in a single-layer network and compared its ability of separating spatio-temporal patterns with that of other rules, including the Hebbian learning rule and its extended rules. These simulated results show that the spatio-temporal learning rule has the highest efficiency in separating different spatio-temporal patterns and may thereby be responsible for temporarily storing and recalling memory. (C) 2004 Elsevier B.V. All rights reserved.