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Memristive model of amoeba learning

Yuriy V. Pershin1,2,*, Steven La Fontaine1, and Massimiliano Di Ventra1,†

  • 1Department of Physics, University of California, San Diego, La Jolla, California 92093-0319, USA
  • 2Department of Physics and Astronomy and USC Nanocenter, University of South Carolina, Columbia, South Carolina 29208, USA

  • *pershin@physics.sc.edu
  • †diventra@physics.ucsd.edu
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Phys. Rev. E 80, 021926 – Published 21 August, 2009Erratum Phys. Rev. E 82, 019904 (2010)

DOI: https://doi.org/10.1103/PhysRevE.80.021926

Abstract

Recently, it was shown that the amoebalike cell Physarum polycephalum when exposed to a pattern of periodic environmental changes learns and adapts its behavior in anticipation of the next stimulus to come. Here we show that such behavior can be mapped into the response of a simple electronic circuit consisting of a LC contour and a memory-resistor (a memristor) to a train of voltage pulses that mimic environment changes. We also identify a possible biological origin of the memristive behavior in the cell. These biological memory features are likely to occur in other unicellular as well as multicellular organisms, albeit in different forms. Therefore, the above memristive circuit model, which has learning properties, is useful to better understand the origins of primitive intelligence.

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Erratum

Erratum: Memristive model of amoeba learning [Phys. Rev. E 80, 021926 (2009)]

Yuriy V. Pershin, Steven La Fontaine, and Massimiliano Di Ventra
Phys. Rev. E 82, 019904 (2010)

Article Text

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Outline

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Phys. Rev. E 80, 021926– Published 21 August, 2009
  • Received 2 January 2009

© 2009 American Physical Society

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