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Memristive model of amoeba's learning Nature Precedings
Yuriy V. Pershin; Steven La Fontaine; Massimiliano Di Ventra.
Recently, behavioural intelligence of the plasmodia of the true slime mold has been demonstrated. It was shown that a large amoeba-like cell Physarum polycephalum subject to a pattern of periodic environmental changes learns and changes its behaviour in anticipation of the next stimulus to come. Currently, it is not known what specific mechanisms are responsible for such behaviour. Here, we show that such 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. We identify a possible microscopic origin of the memristive behaviour in the Physarum polycephalum, which together with the naturally occurring biological...
Tipo: Manuscript Palavras-chave: Ecology; Molecular Cell Biology; Bioinformatics.
Ano: 2008 URL: http://precedings.nature.com/documents/2431/version/1
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Solving mazes with memristors: a massively-parallel approach Nature Precedings
Yuriy V. Pershin; Massimiliano Di Ventra.
Solving mazes is not just a fun pastime. Mazes are prototype models in graph theory, topology, robotics, traffic optimization, psychology, and in many other areas of science and technology. However, when maze complexity increases their solution becomes cumbersome and very time consuming. Here, we show that a network of memristors - resistors with memory - can solve such a non-trivial problem quite easily. In particular, maze solving by the network of memristors occurs in a massively parallel fashion since all memristors in the network participate simultaneously in the calculation. The result of the calculation is then recorded into the memristors’ states, and can be used and/or recovered at a later time. Furthermore, the network of memristors...
Tipo: Manuscript Palavras-chave: Neuroscience.
Ano: 2011 URL: http://precedings.nature.com/documents/5748/version/1
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