An analysis of the use of Hebbian and Anti-Hebbian spike time dependent plasticity learning functions within the context of recurrent spiking neural networks

Neurocomputing

Volumn 72, Issue 4-6, 2009, Pages 685-692

  Carnell, Andrew ^a  

^a University of Bath   (United Kingdom)

Author keywords

Convergence; Hebbian learning; LSM; Spike train storage

Indexed keywords

CONVERGENCE; DEGREE OF STABILITIES; DESIRABLE PROPERTIES; HEBBIAN LEARNING; LEARNING FUNCTIONS; LSM; SPIKE TIME DEPENDENT PLASTICITIES; SPIKE TRAIN STORAGE; SPIKING NEURAL NETS; SPIKING NEURAL NETWORKS; SYNAPTIC WEIGHTS; WEIGHT CHANGES;

EDUCATION; LEAST SQUARES APPROXIMATIONS; PLASTICITY; RECURRENT NEURAL NETWORKS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; LEARNING; NERVE CELL PLASTICITY; PRIORITY JOURNAL; SPIKE; STATISTICAL ANALYSIS; SYNAPTIC EFFICACY; TIME;

EID: 58149472664     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.07.012     Document Type: Article

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