A non-parametric Bayesian approach to spike sorting

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn , Issue , 2006, Pages 1165-1168

  Wood, Frank ^a   Goldwater, Sharon ^b   Black, Michael J ^a  

^a Brown University   (United States)

^b BROWN UNIVERSITY   (United States)

Author keywords

Bayesian inference; Chinese restaurant process; Expectation maximization; Gibbs sampling; Infinite mixture model; Markov chain Monte Carlo; Mixture modeling; Non parametric Bayesian modeling; Spike sorting

Indexed keywords

DATA REDUCTION; MARKOV PROCESSES; MAXIMUM LIKELIHOOD; MONTE CARLO METHODS; OPTIMIZATION; PROBLEM SOLVING;

BAYESIAN INFERENCE; CHINESE RESTAURANT PROCESS; EXPECTATION MAXIMIZATION; GIBBS SAMPLING; INFINITE MIXTURE MODEL; MIXTURE MODELING; NON-PARAMETRIC BAYESIAN MODELING; SPIKE SORTING;

BAYESIAN NETWORKS;

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BRAIN MAPPING; EVALUATION STUDY; HAPLORHINI; METHODOLOGY; NERVE CELL NETWORK; PHYSIOLOGY;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN MAPPING; HAPLORHINI; NERVE NET; PATTERN RECOGNITION, AUTOMATED;

EID: 34047105291     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2006.260700     Document Type: Conference Paper

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