NASS: An empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology

Journal of Neuroscience Methods

Volumn 190, Issue 1, 2010, Pages 129-142

  Adamos, Dimitrios A ^a   Laskaris, Nikolaos A ^b   Kosmidis, Efstratios K ^b   Theophilidis, George ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Extreme Learning Machine; ISOMAP; Semi supervised data learning; Spike sorting

Indexed keywords

ALGORITHM; ARTICLE; ARTIFACT; EXTREME MACHINE LEARNING; FALSE NEGATIVE RESULT; INFORMATION PROCESSING; ISOMETRIC FEATURE MAPPING; MACHINE LEARNING; NERVE CONDUCTION; NERVE POTENTIAL; NOISE ASSOCIATED SPIKE SORTING; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SIGNAL PROCESSING; SPIKE WAVE; VALIDATION PROCESS; ACTION POTENTIAL; ANIMAL; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER SIMULATION; DATA BASE; EVALUATION STUDY; FUZZY LOGIC; NERVE CELL; PHYSIOLOGY; TIME; VALIDATION STUDY;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATABASES AS TOPIC; FUZZY LOGIC; MODELS, NEUROLOGICAL; NEURONS; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS;

EID: 77953714710     PISSN: 01650270     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2010.04.018     Document Type: Article

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