Memristive model of amoeba learning.

By Yuriy V. Pershin, Steven La Fontaine, M. Di Ventra. Physical review. E, Statistical, nonlinear, and soft matter physics, 2008

473 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/2bd2298f991e628c8fb97e1e18343033938b900b

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Citations in the corpus

Memristor-based memory: The sneak paths problem and solutions (2013, methodology): “In addition to be used as a memory element [2, 17–26], the memristor has found many applications in oscillators [27–30], logic and arithmetic circuits [31, 32], programmable analog circuits [33, 34], and in modeling and emulation of natural phenomena [35, 36].”
Spin-transfer torque magnetic memory as a stochastic memristive synapse (2014, background): “In recent years, the exploitation of nanodevices with memory effects (or memristive devices) as synapses in neuromorphic systems has stimulated a growing interest [6]– [17].”
On the mathematical modeling of memristors (2010, methodology): “The Memristor found application in memory [4], biology [5], and spintronic [6].”
Evolution of Plastic Learning in Spiking Networks via Memristive Connections (2012, methodology): “Memristor theory has also been used to model learning in amoeba [35].”
A Versatile Memristor Model With Nonlinear Dopant Kinetics (2011, methodology): “While memristors have already been used to demonstrate primitive artificial intelligence [15], this marks a new era for neuromorphic engineering.”
Memristor-based neural networks (2013, methodology): “In 2009, they presented a model using an LC oscillator and a memristor, which are both simple, passive elements, that demonstrated pattern recognition and event prediction [115].”
Evolving Spiking Networks with Variable Resistive Memories (2014, background): “Other successful applications include the modelling of learning in amoeba (Pershin et al., 2009), as well as pattern recognition by crossbar circuits for robotic control (Mouttet, 2009).”