Hebbian learning in the agglomeration of conducting particles

M. Sperl, A. Chang, N. Weber, and A. Hübler
Phys. Rev. E 59, 3165 – Published 1 March 1999
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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:https://doi.org/10.1103/PhysRevE.59.3165

©1999 American Physical Society

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

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Vol. 59, Iss. 3 — March 1999

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