Hebbian learning in linear-nonlinear networks with tuning curves leads to near-optimal, multi-alternative decision making

Neural Networks

Volumn 24, Issue 5, 2011, Pages 417-426

  McMillen, Tyler ^a   Simen, Patrick ^b   Behseta, Sam ^a  

^a CALIFORNIA STATE UNIVERSITY   (United States)

^b PRINCETON UNIVERSITY   (United States)

Author keywords

Drift diffusion model; Leaky accumulator; Multihypothesis sequential test; Neural network; Sequential ratio test; Synaptic weight learning

Indexed keywords

DRIFT-DIFFUSION MODEL; LEAKY ACCUMULATOR; SEQUENTIAL RATIO TEST; SEQUENTIAL TESTS; SYNAPTIC WEIGHT;

DECISION MAKING; NONLINEAR NETWORKS; OPTIMIZATION; PROBABILITY; STATISTICAL TESTS;

NEURAL NETWORKS;

ARTICLE; AUDITORY DISCRIMINATION; BRAIN FUNCTION; DECISION MAKING; HEBBIAN LEARNING ALGORITHM; HUMAN; LEARNING ALGORITHM; MATHEMATICAL MODEL; NERVE CELL NETWORK; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; RESPONSE TIME; REWARD; SIGNAL DETECTION; STIMULUS RESPONSE; TASK PERFORMANCE; VISUAL STIMULATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DECISION MAKING; HUMANS; LINEAR MODELS; MATHEMATICAL COMPUTING; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; REWARD; SYNAPTIC TRANSMISSION;

EID: 79953682507     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.01.005     Document Type: Article

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