Hebbian learning in the agglomeration of conducting particles

The Hebbian learning rule is a fundamental concept in the learning of a neuronal net, where a frequently used connection of two neurons is continually reinforced. We study the properties of self-assembling connections of conducting particles in a dielectric liquid, and find that the strength of the connection between different electrodes represents a memory for the history of the system. Optimal parameters and sequences of stimulation for effective training are determined. We discuss a future application of our results for the implementation of a nonvolatile neuronal network based on self-assembling nanowires on a semiconductor surface.