A pulse-based reinforcement algorithm for learning continuous functions

An algorithm is presented which allows continuous functions to be learned by a neural network using spike-based reinforcement learning. Both the mean and the variance of the weights are changed during training; the latter is accomplished by manipulating the lengths of the spike trains used to represent real-valued quantifies. The method is here applied to the probabilistic RAM (pRAM) model, but it may be adapted for use with any pulse-based stochastic model in which individual weights behave as random variables.