Slime Mold Periodic Timing with Memristor Modeling

The mathematical modeling of memristors have started its bleedthru into other fields: is reporting in the Quantitative Biology & Cell Behavior a new paper titled Memristive model of amoeba’s learning, by authors Yuriy V. Pershin, Steven La Fontaine, and Massimiliano Di Ventra. Neural nets have been around a long time, even though seemingly of primary interest to stock market marketing schemes :)…

Earlier this year, a group of Japanese scientists reported that with appropriate training, the true slime mold Physarum polycephalum can anticipate the timing of periodic events. That’s more than some politicians can manage and P polycephalum is only a single-celled amoeba, albeit a talented one. A few years ago a Hungarian team showed that slime mold was able to find the shortest way through a maze. Clearly, primitive intelligence has cellular origins but how might this work? Yuriy Pershin at UC San Diego and pals think they know how. They say that this kind of behaviour is identical to the way a simple electronic circuit reacts to train of voltage pulses. The circuit consists of an inductor, capacitor and a memory-resistor, or memristor.[article]

FYI, “P. polycephalum is typically yellow in color, and eats fungal spores, bacteria, and other microbes. P. polycephalum is one of the easiest eukaryotic microbes to grow in culture, and has been used as a model organism for many studies involving amoeboid movement and cell motility. Most organisms receive mitochondrial DNA from their mother, but it is not known from where P. polycephalum receives its mitochondrial DNA as it is currently not possible to distinguish between male and female. It is also believed that the P. polycephalum is the first eukaryotic cell to have organelles such as mitochondria and ribosomal features.” – [wikipedia]

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 oscillators, forms the basis of the amoeba’s learning. These microscopic memristive 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. [abstract]

Perhaps now the work on reinstituting the Perceptron model can return as a form of building specific types of memory-loss into these systems.

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