Conducting polymer-coated<i>Physarum polycephalum</i>towards the synthesis of bio-hybrid electronic devices

Costello, B.J.P. de Lacy; Mayne, Richard; Adamatzky, Andrew

doi:10.1080/03081079.2014.997524

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…The protoplasm has also been interfaced with an FPGA to facilitate implementation of arithmetic calculations and bioelectric measurement 47 . It has been shown that adding nano-particles can alter the conductive properties of protoplasmic tubes 48 49 ; this concept has been extended into producing working electronic components with funtionalised protoplasmic tubes of P. polycephalum 50 .…”

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confidence: 99%

Towards a Physarum learning chip

Whiting

Jones

Bull

et al. 2016

Sci Rep

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Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks.

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mentioning

confidence: 99%

Towards a Physarum learning chip

Whiting

Jones

Bull

et al. 2016

Sci Rep

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Slime Mould Nanotechnology

Mayne

Adamatzky

2016

Advances in Physarum Machines

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Thirty Seven Things to Do with Live Slime Mould

Adamatzky

2016

Emergence, Complexity and Computation

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Slime mould Physarum polycephalum is a large single cell capable for distributed sensing, concurrent information processing, parallel computation and decentralised actuation. The ease of culturing and experimenting with Physarum makes this slime mould an ideal substrate for real-world implementations of unconventional sensing and computing devices. In the last decade the Physarum became a swiss knife of the unconventional computing: give the slime mould a problem it will solve it. We provide a concise summary of what exact computing and sensing operations are implemented with live slime mould. The Physarum devices range from morphological processors for computational geometry to experimental archeology tools, from self-routing wires to memristors, from devices approximating a shortest path to analog physical models of space exploration.

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Conducting polymer-coatedPhysarum polycephalumtowards the synthesis of bio-hybrid electronic devices

Cited by 4 publications

References 32 publications

Towards a Physarum learning chip

Towards a Physarum learning chip

Slime Mould Nanotechnology

Thirty Seven Things to Do with Live Slime Mould

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