Memristive model of amoeba learning

被引:341
作者
Pershin, Yuriy V. [1 ,2 ,3 ]
La Fontaine, Steven [1 ]
Di Ventra, Massimiliano [1 ]
机构
[1] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA
[2] Univ S Carolina, Dept Phys & Astron, Columbia, SC 29208 USA
[3] Univ S Carolina, USC Nanoctr, Columbia, SC 29208 USA
来源
PHYSICAL REVIEW E | 2009年 / 80卷 / 02期
关键词
brain models; cellular biophysics; microorganisms; neural nets; RLC circuits; MECHANISM;
D O I
10.1103/PhysRevE.80.021926
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
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.
引用
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页数:6
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