Neural learning circuits utilizing nano-crystalline silicon transistors and memristors

IEEE Transactions on Neural Networks and Learning Systems

Volumn 23, Issue 4, 2012, Pages 565-573

  Cantley, Kurtis D ^a   Subramaniam, Anand ^a   Stiegler, Harvey J ^a   Chapman, Richard A ^a   Vogel, Eric M ^b  

^a The University of Texas at Dallas   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

Hebbian learning; memristor; nano crystalline silicon; neuromorphic; SPICE; thin film transistor

Indexed keywords

ASSOCIATIVE LEARNING; COINCIDENCE DETECTION; FUNDAMENTAL FREQUENCIES; FUNDAMENTAL PROPERTIES; HEBBIAN LEARNING; MEMRISTOR; NEUROMORPHIC; SYNAPTIC CONNECTIONS;

COINCIDENCE CIRCUITS; INTEGRATED CIRCUIT MANUFACTURE; MEMRISTORS; NEURONS; PASSIVE FILTERS; RESISTORS; THIN FILM TRANSISTORS;

SPICE;

ANIMAL; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOMIMETICS; COMPUTER AIDED DESIGN; COMPUTER SIMULATION; DATA STORAGE DEVICE; DEVICE FAILURE ANALYSIS; DEVICES; EQUIPMENT DESIGN; HUMAN; IMPEDANCE; NANOTECHNOLOGY; NERVE CELL NETWORK; PHYSIOLOGY; SEMICONDUCTOR;

ANIMALS; ARTIFICIAL INTELLIGENCE; BIOMIMETICS; COMPUTER SIMULATION; COMPUTER STORAGE DEVICES; COMPUTER-AIDED DESIGN; ELECTRIC IMPEDANCE; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; HUMANS; MODELS, NEUROLOGICAL; NANOTECHNOLOGY; NERVE NET; NEURAL NETWORKS (COMPUTER); TRANSISTORS, ELECTRONIC;

EID: 84874044335     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2012.2184801     Document Type: Article

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