Pulse stream neural networks and reinforcement learning

A neural network model based on pulse signals is presented. Pulsed signals are shown to give the model such properties as a simplified hardware implementation, the ability to use stochastic search techniques, and biological plausibility. In this model, there is also the possibility of building modular neural systems. Since information representation in pulsed signals has to be studied carefully, the stochastic representation is outlined and it is shown how generation of a signal and estimation of information are obtained. Several design issues have to be solved before it is possible to use a model of this kind, and solutions are proposed for synaptic multiplication, summation, and nonlinearity. It is also shown that reinforcement learning is the type of learning which is best suited for this type of model and that this does not interfere with the properties mentioned above. Initial simulations of the model show promising results