Biologically plausible learning in neural networks: A lesson from bacterial chemotaxis

Biological Cybernetics

Volumn 101, Issue 5-6, 2009, Pages 379-385

  Shimansky, Yury P ^a  

^a Arizona State University   (United States)

Author keywords

Bacterial chemotaxis; Biological optimization; Learning; Modification probability; Neural networks

Indexed keywords

ABRUPT CHANGE; ARTIFICIAL NEURAL NETWORK; BACTERIAL CHEMOTAXIS; BIOPHYSICAL MECHANISM; COMPUTATIONAL EXPERIMENT; COMPUTATIONAL MODELING; CONVENTIONAL METHODS; ERROR BACK-PROPAGATION; EXPERIMENTAL ANALYSIS; EXPERIMENTAL OBSERVATION; GENERAL METHOD; LEARNING PROCESS; MULTI-DIMENSIONAL SPACE; NEURAL NETWORK OPTIMIZATION; OBJECTIVE FUNCTIONS; OPTIMALITY CRITERIA; PROBABILITY NEURAL NETWORK; SPATIAL LOCATION; SYNAPTIC PLASTICITY; SYNAPTIC WEIGHT; TEMPORAL GRADIENTS;

BACKPROPAGATION; BACTERIA; BACTERIOLOGY; BIOCHEMISTRY; EDUCATION; OPTIMIZATION; PROBABILITY;

NEURAL NETWORKS;

BACTERIA (MICROORGANISMS);

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACTERIUM; BIOLOGICAL MODEL; CHEMOTAXIS; COMPUTER SIMULATION; CYTOLOGY; LEARNING; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY; SYNAPSE;

BACTERIA; CHEMOTAXIS; COMPUTER SIMULATION; LEARNING; MODELS, NEUROLOGICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS; SYNAPSES;

EID: 72149099584     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-009-0341-6     Document Type: Article

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