Implementing Hebbian Learning in a Rank-Based Neural Network
Recent works have shown that biologically motivated net works of spiking neurons can potentially process information very quickly by encoding information in the latency at which different neurons fire, rather than by using frequency of firing as the code. In this paper, the relevant information is the rank vector of latency order of competing neurons. We propose here a Hebbian reinforcement, learning scheme to adjust the weights of a terminal layer of decision neurons in order to process this information. Then this learning rule is shown to be efficient in a simple pattern recognition task. We discuss in conclusion further extensions of that learning strategy for artificial vision.