Neurodynamics A spiking neural network implementation of path finding

We propose an implementation of Izhikevich spiking neural networks to solve a 2D path finding problem. Given a 2D grid of size nxn, we can solve the path finding problem with a spiking neural network consisting of n2 neural populations. We propose that the activation of a population encodes the exploration of a certain state with convergence encoding the next best state. The series of next best states terminating at the destination node is indeed the shortest path to the destination. This paper discusses the theoretical foundation of this research along with a simplified experiment for a fully connected 2x2 graph graph.