A learning algorithm to teach spatiotemporal patterns to recurrent neural networks

摘要

A new supervised learning algorithm is proposed. It teaches spatiotemporal patterns to the recurrent neural network with arbitrary feedback connections. In this method the network with fixed connection weights is run for a given period of time under a given external input and initial condition. Then the weights are changed so that the total error from the time dependent teacher signal in this period is maximally decreased. This algorithm is equivalent to the back propagation method for the recurrent network if the discrete time prescription is adopted. However, continuous time formalism seems suited for temporal processing application.