A forecast-based STDP rule suitable for neuromorphic implementation

Neural Networks

Volumn 32, Issue , 2012, Pages 3-14

  Davies, S ^a   Galluppi, F ^a   Rast, A D ^a   Furber, S B ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Forecast; Izhikevich; Neuromorphic; Spiking; SpiNNaker; STDP; Time To Spike; Unsupervised learning

Indexed keywords

FORECAST; IZHIKEVICH; NEUROMORPHIC; SPIKING; SPINNAKER; STDP; TIME TO SPIKE;

FORECASTING; HARDWARE; INTELLIGENT AGENTS; NEURAL NETWORKS; UNSUPERVISED LEARNING;

APPROXIMATION ALGORITHMS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; FORECASTING; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL PLASTICITY; POISSON DISTRIBUTION; POSTSYNAPTIC POTENTIAL; PREDICTION; PRIORITY JOURNAL; PROBLEM SOLVING; PROCESS DEVELOPMENT; SENSITIVITY AND SPECIFICITY; SIGNAL NOISE RATIO; SIGNAL PROCESSING; SIMULATION; SPIKE TIMING DEPENDENT PLASTICITY ALGORITHM; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; COMPUTER SYSTEMS; COMPUTERS; LONG-TERM POTENTIATION; MEMBRANE POTENTIALS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; SOFTWARE; SYNAPTIC TRANSMISSION;

EID: 84861770528     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.02.018     Document Type: Article

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