| Document Date: 2012-09-24 10:51:12
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City Reading / Bremen / / Company Cambridge University Press / Hertz / / Country Germany / United States / United Kingdom / / Currency pence / / / Facility Stefan Bornholdt† Institute / University of Bremen / / IndustryTerm introduced networks / adaptive networks / spin type networks / cell-cycle networks / mechanics tools / ssion yeast cell-cycle control networks / genetic networks / binary threshold networks / self-organized critical neural networks / protein network / biological networks / protein networks / adaptive algorithm / dynamical networks / threshold networks / sample networks / biological regulatory networks / neural networks / biological systems / model networks / dynamical network / supercritical networks / random threshold networks / gene regulatory networks / spin systems / binary threshold network / regulatory networks / neural information processing / nite size systems / power-law distributed activity avalanches / spin-type threshold networks / yeast cell cycle network / random networks / specific chemical component / defined threshold network / magnetic systems / molecular information processing networks / biochemical networks / power-law increase / / Organization Cambridge University / American Physical Society / Rybarsch* and Stefan Bornholdt† Institute for Theoretical Physics / University of Bremen / / Person Nat / Matthias Rybarsch / Stefan Bornholdt / / Position model for neural information processing / template model for biological systems / minimal model for neural networks / / Product Bremen / / ProvinceOrState Massachusetts / / PublishedMedium PLoS ONE / Physica D / Physica A / / Technology neural network / simulation / adaptive algorithm / /
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PHYSICAL REVIEW E 86, Binary threshold networks as a natural null model for biological networks Matthias Rybarsch* and Stefan Bornholdt† Institute for Theoretical Physics, University of Bremen, DB