Storage and retrieval of complex sequences in neural networks
Isabelle Guyon, L. Personnaz, Jean‐Pierre Nadal, Gérard Dreyfus · Physical Review A · 1988
The storage and retrieval of complex sequences, with bifurcation points, for instance, in fully connected networks of formal neurons, is investigated. We present a model which involves the transmission of informations undergoing various delays from all neurons to one neuron, through synaptic connections, possibly of high order. Assuming parallel dynamics, an exact solution is proposed; it allows one to store without errors a number of elementary transitions which are of the order of the number of synaptic connections related to one neuron. A fast-learning algorithm, requiring a single presentation of the prototype sequences, is derived; it guarantees the exact storage of the transitions. It is shown that local learning procedures with repeated presentations, used for pattern storage, can be generalized to sequence storage.