Slime mould processors, logic gates and sensors

A heterotic, or hybrid, computation implies that two or more substrates of different physical nature are merged into a single device with indistinguishable parts. These hybrid devices then undertake coherent acts on programmable and sensible processing of information. We study the potential of heterotic computers using slime mould acting under the guidance of chemical, mechanical and optical stimuli. Plasmodium of acellular slime mould Physarum polycephalum is a gigantic single cell visible to the unaided eye. The cell shows a rich spectrum of behavioural morphological patterns in response to changing environmental conditions. Given data represented by chemical or physical stimuli, we can employ and modify the behaviour of the slime mould to make it solve a range of computing and sensing tasks. We overview results of laboratory experimental studies on prototyping of the slime mould morphological processors for approximation of Voronoi diagrams, planar shapes and solving mazes, and discuss logic gates implemented via collision of active growing zones and tactile responses of P. polycephalum. We also overview a range of electronic components—memristor, chemical, tactile and colour sensors—made of the slime mould.

[1]  Bartosz A Grzybowski,et al.  Maze solving by chemotactic droplets. , 2010, Journal of the American Chemical Society.

[2]  N. Kamiya,et al.  Bioelectric phenomena in the myxomycete plasmodium and their relation to protoplasmic flow , 1950 .

[3]  Andrew I. Adamatzky,et al.  Assessing the chemotaxis behavior of Physarum polycephalum to a range of simple volatile organic chemicals , 2013, Communicative & integrative biology.

[4]  T. Nakagaki,et al.  Intelligence: Maze-solving by an amoeboid organism , 2000, Nature.

[5]  I Block,et al.  Blue light as a medium to influence oscillatory contraction frequency in Physarum. , 1981, Cell biology international reports.

[6]  Andrew Adamatzky,et al.  Towards constructing one-bit binary adder in excitable chemical medium , 2010, 1010.4694.

[7]  Andrew Adamatzky,et al.  Simulating strange attraction of acellular slime mould Physarum polycephaum to herbal tablets , 2012, Math. Comput. Model..

[8]  A. Tero,et al.  Minimum-risk path finding by an adaptive amoebal network. , 2007, Physical review letters.

[9]  Andrew Adamatzky,et al.  On the Internalisation, Intraplasmodial Carriage and Excretion of Metallic Nanoparticles in the Slime Mould, Physarum Polycephalum , 2011, Int. J. Nanotechnol. Mol. Comput..

[10]  Herbert Edelsbrunner,et al.  Three-dimensional alpha shapes , 1992, VVS.

[11]  Andrew Adamatzky,et al.  Manipulating substances with Physarum polycephalum , 2010 .

[12]  Andrew Adamatzky,et al.  Slime Mould Memristors , 2013, 1306.3414.

[13]  O. Hamill,et al.  Molecular basis of mechanotransduction in living cells. , 2001, Physiological reviews.

[14]  Andrew Adamatzky,et al.  On attraction of slime mould Physarum polycephalum to plants with sedative properties , 2011 .

[15]  Andrew Adamatzky,et al.  Hot ice computer , 2009, 0908.4426.

[16]  Andrew Adamatzky,et al.  Evolution of Plastic Learning in Spiking Networks via Memristive Connections , 2012, IEEE Transactions on Evolutionary Computation.

[17]  S. Stephenson,et al.  Myxomycetes: A Handbook of Slime Molds , 1994 .

[18]  T. Nakagaki,et al.  Path finding by tube morphogenesis in an amoeboid organism. , 2001, Biophysical chemistry.

[19]  Andrew Adamatzky,et al.  Physarum Machines: Computers from Slime Mould , 2010 .

[20]  Ray A. Jarvis,et al.  On the Identification of the Convex Hull of a Finite Set of Points in the Plane , 1973, Inf. Process. Lett..

[21]  B. Kirby Micro- and nanoscale fluid mechanics : transport in microfluidic devices , 2010 .

[22]  W. Tian,et al.  Introduction to Microfluidics , 2008 .

[23]  F. Ellinger,et al.  A Memristive Model Compatible with Triplet Rule for Spike-Timing-Dependent-Plasticity , 2011, 1108.4299.

[24]  Toshinori Munakata,et al.  Flow resistance for microfluidic logic operations , 2004 .

[25]  R. Meyer,et al.  Studies on microplasmodia of Physarum polycephalum V: electrical activity of different types of microplasmodia and macroplasmodia. , 1979, Cell biology international reports.

[26]  Andrew Adamatzky,et al.  Boolean Logic Gates from a Single Memristor via Low-Level Sequential Logic , 2013, UCNC.

[27]  Charles M Schroeder,et al.  Microfluidic Wheatstone bridge for rapid sample analysis. , 2011, Lab on a chip.

[28]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[29]  L. Chua Memristor-The missing circuit element , 1971 .

[30]  Tetsuya Asai,et al.  Reaction-diffusion computers , 2005 .

[31]  I Block,et al.  The pathway of photosensory transduction in Physarum polycephalum. , 1981, Cell biology international reports.

[32]  W Seifriz A theory of protoplasmic streaming , 1937, Protoplasma.

[33]  Roger D Kamm,et al.  On the molecular basis for mechanotransduction. , 2004, Mechanics & chemistry of biosystems : MCB.

[34]  Andrew Adamatzky,et al.  Slime mould logical gates: exploring ballistic approach , 2010, 1005.2301.

[35]  L. V. Heilbrunn,et al.  The Electric Charge of Protoplasmic Colloids , 1939, Physiological Zoology.

[36]  Toshinori Munakata,et al.  Micro/nanofluidic computing , 2007, CACM.

[37]  Andrew Adamatzky,et al.  Slime mould tactile sensor , 2013, ArXiv.

[38]  Andrew Adamatzky,et al.  Towards slime mould colour sensor: Recognition of colours by Physarum polycephalum , 2013, ArXiv.

[39]  A. Adamatzky Tactile Bristle Sensors Made With Slime Mold , 2014, IEEE Sensors Journal.

[40]  Andreas Manz,et al.  Glow discharge in microfluidic chips for visible analog computing. , 2002, Lab on a chip.

[41]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[42]  D. Gradmann,et al.  Electrical properties of the plasma membrane of microplasmodia ofPhysarum polycephalum , 2005, The Journal of Membrane Biology.

[43]  Andrew Adamatzky,et al.  Towards slime mould chemical sensor: Mapping chemical inputs onto electrical potential dynamics of Physarum Polycephalum , 2013, ArXiv.

[44]  T. Iwamura,et al.  Correlations between protoplasmic streaming and bioelectric potential of a slime mold, Physarum polycephalum , 1949 .

[45]  K. Jacobs Introduction to Microfluidics. By Patrick Tabeling. , 2006 .

[46]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[47]  H. P. Rusch,et al.  Sporulation in Physarum polycephalum: a model system for studies on differentiation. , 1969, Experimental cell research.

[48]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[49]  Andrew Adamatzky,et al.  Organic memristor Devices for Logic Elements with Memory , 2012, Int. J. Bifurc. Chaos.

[50]  Andrew Adamatzky,et al.  Observation, Characterization and Modeling of Memristor Current Spikes , 2013, 1302.0771.

[51]  Andrew Adamatzky,et al.  Slime mold microfluidic logical gates , 2014 .

[52]  U. Kishimoto,et al.  RHYTHMICITY IN THE PROTOPLASMIC STREAMING OF A SLIME MOLD, PHYSARUM POLYCEPHALUM , 1958, The Journal of general physiology.

[53]  David G. Kirkpatrick,et al.  On the shape of a set of points in the plane , 1983, IEEE Trans. Inf. Theory.

[54]  R. Stanley Williams Aftermath of Finding the Memristor , 2014 .

[55]  Andrew Adamatzky,et al.  Slime mould computes planar shapes , 2011, Int. J. Bio Inspired Comput..

[56]  H. P. Rusch,et al.  Morphological observations on growth and differentation of Physarum polycephalum grown in pure culture. , 1961, Developmental biology.

[57]  Tatiana Berzina,et al.  Polymeric electrochemical element for adaptive networks: Pulse mode , 2008 .