Ensemble fractional sensitivity: A quantitative approach to neuron selection for decoding motor tasks

Computational Intelligence and Neuroscience

Volumn 2010, Issue , 2010, Pages

  Singhal, Girish ^a   Aggarwal, Vikram ^a   Acharya, Soumyadipta ^a   Aguayo, Jose ^b   He, Jiping ^b   Thakor, Nitish ^a  

^a Johns Hopkins University   (United States)

^b Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECODING ALGORITHM; FIRING RATES; IDENTIFICATION ACCURACY; INPUT SPACE; MODEL BASED APPROACH; MOTOR TASKS; NEURON SELECTION; NOISY NEURON; OPTIMAL NUMBER; QUANTITATIVE APPROACH; RANDOM SUBSETS; REACH TO GRASP; RELATIVE CONTRIBUTION; RHESUS MONKEY; ROBUST METHODS; TRAINING DATA; TRAINING DATA SETS;

COMPUTER SIMULATION; NEURONS; SENSITIVITY ANALYSIS;

DECODING;

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; FRONTAL LOBE; HAND; MACACA; MALE; MONTE CARLO METHOD; MOTOR ACTIVITY; MOTOR CORTEX; NERVE CELL; NONLINEAR SYSTEM; PHYSIOLOGY; SIGNAL PROCESSING; WRIST;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; COMPUTER SIMULATION; FRONTAL LOBE; HAND; MACACA MULATTA; MALE; MONTE CARLO METHOD; MOTOR ACTIVITY; MOTOR CORTEX; NEURAL NETWORKS (COMPUTER); NEURONS; NONLINEAR DYNAMICS; SIGNAL PROCESSING, COMPUTER-ASSISTED; WRIST;

EID: 77951624534     PISSN: 16875265     EISSN: 16875273     Source Type: Journal    
DOI: 10.1155/2010/648202     Document Type: Article

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