A network of spiking neurons that can represent interval timing: Mean field analysis

Journal of Computational Neuroscience

Volumn 30, Issue 2, 2011, Pages 501-513

  Gavornik, Jeffrey P ^a,b,c   Shouval, Harel Z ^a  

^a UNIVERSITY OF TEXAS MEDICAL SCHOOL   (United States)

^b The University of Texas at Austin   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Mean field theory; Recurrent network; Reinforcement learning; Spontaneous activity; Synaptic plasticity

Indexed keywords

ENCODING (SYMBOLS); NEURONS; REINFORCEMENT LEARNING; STOCHASTIC SYSTEMS;

MEAN FIELD ANALYSIS; MEAN-FIELD THEORIES; NEURAL MECHANISMS; RECURRENT NETWORKS; REINFORCEMENT LEARNINGS; SPIKING NEURONES; SPONTANEOUS ACTIVITY; SYNAPTIC PLASTICITY; TEMPORAL INFORMATION; TEMPORAL REPRESENTATIONS;

MEAN FIELD THEORY;

ANALYTIC METHOD; ARTICLE; ASSOCIATION; CELL ACTIVATION; LINEAR SYSTEM; MEAN FIELD THEORY ANALYSIS; MOLECULAR DYNAMICS; NERVE CELL EXCITABILITY; NERVE CELL INHIBITION; NERVE CELL NETWORK; NERVE CELL PLASTICITY; NERVE CELL STIMULATION; NERVE PROJECTION; NEUROFEEDBACK; PREDICTION; PRIORITY JOURNAL; REINFORCEMENT; REWARD; SIGNAL DETECTION; SPIKE WAVE; STEADY STATE; STIMULUS RESPONSE; STOCHASTIC MODEL; TASK PERFORMANCE; TIME PERCEPTION; VISUAL CORTEX; VISUAL DISCRIMINATION; VISUAL STIMULATION; WORKING MEMORY;

EID: 79953303199     PISSN: 09295313     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10827-010-0275-y     Document Type: Article

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