Scinovex
article Open AccessTop 1% cited

Memristive model of amoeba learning

Physical Review E · 2009 · Vol. 80(2) · pp. 021926–021926
Yuriy V. PershinSteven La FontaineMassimiliano Di Ventra

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.

Slime Mold and Myxomycetes ResearchAdvanced Memory and Neural ComputingPlant and Biological Electrophysiology StudiesPhysarum polycephalumMemristorMulticellular organismComputer scienceBiological clockStimulus (psychology)Biological systemNeuroscienceArtificial intelligenceBiology

MeSH terms

AmoebaAnimalsElectric ConductivityIntelligenceMemoryModels, NeurologicalPhysarum polycephalum
Citations
483
FWCI
24.19
field-weighted impact
References
17
Percentile
100%
vs. same field & year
Citations per year
References
Memristive switching mechanism for metal/oxide/metal nanodevices
Nature Nanotechnology · 2008 · 2,920 citations
Memristive devices and systems
Proceedings of the IEEE · 1976 · 2,537 citations
The missing memristor found
Nature · 2008 · 11,249 citations
Citation Network

How this paper connects to the literature. Drag to explore, click any node to open that paper.