Self-organizing spiking neural model for learning fault-tolerant spatio-motor transformations

IEEE Transactions on Neural Networks and Learning Systems

Volumn 23, Issue 10, 2012, Pages 1526-1538

  Srinivasa, Narayan ^a   Cho, Youngkwan ^a  

^a Hughes Aircraft Co   (United States)

Author keywords

Kinematics; learning; neurons; robots; spatio motor transformations; spike timing dependent plasticity (STDP); synapses

Indexed keywords

2 DEGREE OF FREEDOMS; FORWARD AND INVERSE KINEMATICS; INTEGRATE-AND-FIRE NEURONS; LEARNING; MULTILAYERED ARCHITECTURE; SPIKE-TIMING-DEPENDENT PLASTICITY; SPIKING NEURAL MODELS; SYNAPSES;

INVERSE KINEMATICS; KINEMATICS; NEURONS; ROBOTS;

COMPUTER SIMULATION;

ACTION POTENTIAL; ANIMAL; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOMIMETICS; COMPUTER SIMULATION; DEVICES; HUMAN; MOTONEURON; MOVEMENT (PHYSIOLOGY); NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY; PROCEDURES; PROPRIOCEPTION; ROBOTICS;

ACTION POTENTIALS; ANIMALS; BIOMIMETICS; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; MOTOR NEURONS; MOVEMENT; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS; PROPRIOCEPTION; ROBOTICS;

EID: 84876943054     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2012.2207738     Document Type: Article

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