title: Stochastic resonance in an intracellular genetic perceptron.
abstract: Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity.
authors.affiliation: Department of Mathematics, University College London, United Kingdom.
authors.forename: Russell
authors.initials: R
authors.lastname: Bates
authors.affiliation: Institute for Women's Health, University College London, United Kingdom.
authors.forename: Oleg
authors.initials: O
authors.lastname: Blyuss
authors.affiliation: Department of Mathematics, University College London, United Kingdom and Institute for Women's Health, University College London, United Kingdom and Department of Mathematics, King AbdulAziz University, Jeddah, Saudi Arabia and Lobachevsky State University of Nizhniy Novgorod, Nizhniy Novgorod, Russia.
authors.forename: Alexey
authors.initials: A
authors.lastname: Zaikin
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D005786:Gene Expression Regulation / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020543:Proteome / Q000235:genetics*; D013269:Stochastic Processes; D014158:Transcription, Genetic / Q000235:genetics*
chemical_list: D020543:Proteome