Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors

It is shown that a simple modification of synaptic structures (of the Hopfield type) constructed to produce autoassociative attractors, produces neural networks whose attractors are correlated with several (learned) patterns used in the construction of the matrix. The modification stores in the matrix a fixed sequence of uncorrelated patterns. The network then has correlated attractors, provoked by the uncorrelated stimuli. Thus, the network converts the temporal order (or temporal correlation) expressed by the sequence of patterns, into spatial correlations expressed in the distributions of neural activities in attractors. The model captures phenomena observed in single electrode recordings in performing monkeys by Miyashita