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
        }
}
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… Expand
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Memristive and Memcapacitive Models of Physarum Learning
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An electronic implementation of amoeba anticipation
In nature, the capability of memorizing environmental changes and recalling past events can be observed in unicellular organisms like amoebas. Pershin and Di Ventra have shown that such learning… Expand
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It is demonstrated that protoplasmic tubes of the acellular slime mould Physarum polycephalum show current versus voltage profiles consistent with memristive systems, and that this effect is due to the living protoplasma of the mould, which contributes to the development of self-growing bio-electronic circuits. Expand
Mimicking Spike-Timing-Dependent Plasticity with Emulated Memristors
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This paper proposes a basic memristor emulation architecture and shows that it can be used to mimic certain characteristics of synapses and postulates that emulation has the advantage of working with real-world circuits and not stylized simulation models. Expand
Autowaves in Memristive Cellular Neural Networks
TLDR
A Cellular Neural/Nonlinear Network based on memristive cells for autowave generation and Simulation results show that the system is able to generate autowaves and open the way to the study of other complex phenomena like spiral waves or pattern formation in Memristive circuits. Expand
Antimonotonicity, Crisis and Multiple Attractors in a Simple Memristive Circuit
TLDR
The proposed circuit is the simplest memristive circuit containing the specific circuitry realization of the memristor to be so far presented in the literature, and characterization of the proposed circuit confirms its complex dynamic behavior. Expand
The memristor as an electric synapse - synchronization phenomena
TLDR
This work has studied using computer simulations, the dynamic behavior of two coupled, via a memristor, identical nonlinear circuits, which play the role of “neurons”. Expand
Memristor-based neural networks
TLDR
This work presents and explains the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determines the minimal requirements for an artificial neural network. Expand
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References

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It is shown that memristive behaviour can be mapped into the response of a simple electronic circuit consisting of an LC contour and a memory-resistor (a memristor) to a train of voltage pulses that mimic environment changes. Expand
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