Abstract
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.
- Received 2 July 1998
DOI:dx.doi.org/10.1103/PhysRevE.59.3165
Authors & Affiliations
M. Sperl, A. Chang, N. Weber, and A. Hübler
- Center for Complex Systems Research, Department of Physics, Beckman Institute, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801