Neural networks that learn temporal sequences by selection

Abstract

A model for formal neural networks that learn temporal sequences by selection is proposed on the basis of observations on the acquisition of song by birds, on sequence-detecting neurons, and on allosteric receptors. The model relies on hypothetical elementary devices made up of three neurons, the synaptic triads, which yield short-term modification of synaptic efficacy through heterosynaptic interactions, and on a local Hebbian learning rule. The functional units postulated are mutually inhibiting clusters of synergic neurons and bundles of synapses. Networks formalized on this basis display capacities for passive recognition and for production of temporal sequences that may include repetitions. Introduction of the learning rule leads to the differentiation of sequence-detecting neurons and to the stabilization of ongoing temporal sequences. A network architecture composed of three layers of neuronal clusters is shown to exhibit active recognition and learning of time sequences by selection: the network spontaneously produces prerepresentations that are selected according to their resonance with the input percepts. Predictions of the model are discussed.