Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity-symmetry breaking

Biological Cybernetics

Volumn 101, Issue 2, 2009, Pages 103-114

  Gilson, Matthieu ^a   Burkitt, Anthony N ^a,b,c   Grayden, David B ^a,b,c   Thomas, Doreen A ^a,c   Van Hemmen, J Leo ^d  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b BIONIC EAR INSTITUTE   (Australia)

^c CSIRO   (Australia)

^d TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Learning; Recurrent neuronal network; Spike time correlation; STDP; Symmetry breaking

Indexed keywords

LEARNING; RECURRENT NEURONAL NETWORK; SPIKE-TIME CORRELATION; STDP; SYMMETRY BREAKING;

ASYMPTOTIC ANALYSIS; NEURAL NETWORKS; NEURONS; PLASTICITY; POISSON DISTRIBUTION; SIGNAL ENCODING; TIME MEASUREMENT;

LAKES;

ACTION POTENTIAL; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER SIMULATION; LEARNING; NERVE CELL; NERVE CELL NETWORK; NERVE CELL PLASTICITY; PHYSIOLOGY; POISSON DISTRIBUTION; SYNAPSE; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ANIMALS; COMPUTER SIMULATION; LEARNING; MODELS, NEUROLOGICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; POISSON DISTRIBUTION; SYNAPSES; SYNAPTIC TRANSMISSION;

EID: 68949214346     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-009-0320-y     Document Type: Article

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