Information processing by slime moldsSlime molds!Eeeew! What is it?PowerPoint PresentationWhy study them?Information ProcessingEfficient Pathfinding?SMT and CYCSlide 9Maze SolvingPhysical principlesMathematical modelEvolution of networkResponse to stimuliSlide 15Anticipation of eventsExplanation: biological oscillatorsWhat does all this mean?ReferencesInformation processing by slime moldsFrances TaschukMay 5, 2008Slime molds!“Dog Vomit”“Pretzel Slime Mold” (Hemitrichia serpula)Eeeew! What is it?•Kingdom Protista–True slime molds: Phylum Myxomycota–Cellular slime molds: Phylum Acrasiomycota •True slime molds: nucleus replicates without dividing to form multinucleated feeding massWhy study them?•Single, giant, multinucleated cell–Up to 20 meters in diameter!•Biological information processing–Cell integrates sensory information and develops response–Solve maze–Minimal risk path–Robot control•Phototactic and chemotactic•Easily motivated by oats Information Processing•“Intelligence” without a brain•Constraints:–Absorb nutrients–Maintain intracellular communication (remain connected)–Limit body massEfficient Pathfinding?1. Grow Physarum on agar (forms plasmodium)2. Add food sources (oats) at specific points3. Wait & take picturesSMT and CYC•SMT = Steiner’s minimum tree:graph with least sum of edge lengths (NP-complete problem)•CYC = plasmodium forms cyclical network•Minimum tube length vs robustness SMT-likei) SMT-likeii) combinationDifferent restraint: risk presented by light–Produces reactive oxygen when exposed to light extension velocity slows–Physarum demonstrates negative phototaxisIn pictures d,e,f: upper part of agar is illuminatedMaze SolvingVideo: http://video.google.com/videoplay?docid=-5425792330054733444&q=physarum&ei=3ycaSOHuL52cqQLS_9TfAQPhysical principles•Mathematical model: feedback between thickness of tube and flux through it–More flux leads to wider tube•Cytoplasmic streaming driven by rhythmic contractions of organism produces sheer stress to organize tubesMathematical model•Cytosol is “shuttled” back and forth through the tubes--most of the slime mold’s mass is at the food sources•Network of tubes “evolves” - conductivity D changes depending on flux through tubePressure difference between ends of tubeViscosity of sol Length of tubeRadius of tubeFluxEvolution of network•Positive feedback:•Leads to:–Dead end cutting–Selection of solution path from other possibilitiesconductivityfluxResponse to stimuli•Cellular control of robots•Cells have a lot of computational power—inefficient to emulate biological processing using a computer–Plasticity of living cells: brownian motion explores state space; conformational state change allows for signallingAnticipation of events•Changes in growth rates at different temperatures/humidities–Grow for a few hours, then periodically stimulate with cooler and drier temperatures–Result: growth slows periodically even when not stimulatedExplanation: biological oscillators•Locomotion depends on sum of oscillations•“Memorizes” periodicity•Elements of brain function: memory and anticipationWhat does all this mean?•Parallel dynamics (movement of sol in different parts of protoplasm) lead to information processing - no central processing unit required–Biology takes advantage of this!•Nonlinear dynamics (oscillators) could help explain how biological systems develop intelligent behavior for survival•Information processing power of biological cells may make them more adaptable than conventionally programmed robotsReferences•Nakagaki, T., Iima, M., Ueda, T., Nishiura, Y., Saigusa, T., Tero, A., Kobayashi, R., Showalter, K. 2007. Minimum-risk path finding by an adaptive amoebal network. Physical Review Letters 99.•Nakagaki, T., Kobayashi, R., Nishiura, Y., Ueda, T. 2004. Obtaining multiple separate food sources: behavioural intelligence in the Physarum plasmodium. Proc. R. Soc. B. 271: 2305-2310.•"Slime Molds," Microsoft® Encarta® Online Encyclopedia 2007•Tero, A., Kobayashi, R., Nakagaki, T. 2007. A mathematical model for adaptive transport network in path finding by true slime mold. Journal of Theoretical Biology 244: 553-564.•Tero, A., Nakagaki, T. 2008. Amoebae anticipate periodic events. Physical Review Letters 100: 018101.•Tsuda, S., Zauner, K-P., Gunji, Y-P. 2006. Robot control with biological cells. Biosystems 87:
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