Solving graph algorithms with networks of spiking neurons

Spatio-temporal coding that combines spatial constraints with temporal sequ encing is of great interest to brain-like circuit modelers. In this paper w e present some new ideas of how these types of circuits can self-organize, We introduce a temporal correlation rule based on the time difference betwe en the firings of neurons. With the aid of this rule we show an analogy bet ween a graph and a network of spiking neurons, The shortest path, clusterin g based on the nearest neighbor, and the minimal spanning tree algorithms a re solved using the proposed approach.