Computing with networks of spiking neurons on a biophysically motivated floating-gate based neuromorphic integrated circuit

Neural Networks

Volumn 45, Issue , 2013, Pages 39-49

  Brink, S ^a   Nease, S ^a   Hasler, P ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Floating gate transistor; Neuromorphic VLSI; Single transistor learning synapse; Spiking winner take all; Synfire chain

Indexed keywords

FLOATING GATE TRANSISTORS; NEUROMORPHIC VLSI; SINGLE TRANSISTORS; SYNFIRE CHAINS; WINNER TAKE ALLS;

HARDWARE; NEURAL NETWORKS; NUMERICAL METHODS;

INTEGRATED CIRCUITS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; INTEGRATED CIRCUIT; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL NETWORK; NERVE CONDUCTION; PATTERN RECOGNITION; POWER ANALYSIS; PRIORITY JOURNAL;

FLOATING-GATE TRANSISTOR; NEUROMORPHIC VLSI; SINGLE TRANSISTOR LEARNING SYNAPSE; SPIKING WINNER-TAKE-ALL; SYNFIRE CHAIN;

ACTION POTENTIALS; ANIMALS; BIOPHYSICS; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; SYNAPSES;

EID: 84880790004     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2013.02.011     Document Type: Article

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