Hebbian learning in the agglomeration of conducting particles

M. Sperl, A. Chang, N. Weber, A. Hübler

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)3165-3168
Number of pages4
JournalPhysical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Issue number3
StatePublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


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