Learning Sequential Patterns by Nonmonotone Analog Neural Networks

Masahiko Morita

Abstract: A learning method for nonmonotone analog neural networks is presented by which almost any sequential pattern can be memorized. This method does not require a complex learning rule or particular devices for synchronizing neurons or delay; one only has to change the input pattern gradually and modify the synaptic weights according to a kind of correlation learning rule. Then the state of the network follows a little after the input pattern, its trajectory growing into a dynamic attractor with a few times of repetition. Numerical simulations are performed to examine the learning process and the recollection ability of the model.