Storage of multiple navigation maps using neural networks

Abstract

We present a way to store and to recall different environment navigation maps in a neural network. The model is built upon the idea that a navigation map can be written as the solution of the Laplace's problem with suitable boundary conditions applied to obstacles and goals in the environment. The inherent compression of information coming from that allows us to have good storage performances with a reduced number of synaptic connections.