• DOI:10.1103/PhysRevE.80.021926
• Corpus ID: 9820970

Memristive model of amoeba learning.

@article{Pershin2009MemristiveMO,
  title={Memristive model of amoeba learning.},
  author={Yuriy V. Pershin and Steven La Fontaine and Massimiliano Di Ventra},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2009},
  volume={80 2 Pt 1},
  pages={
          021926
        }
}

• Y. Pershin, Steven La Fontaine, M. Di Ventra
• Published 22 October 2008
• Biology
• Physical review. E, Statistical, nonlinear, and soft matter physics

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.