A new look at state-space models for neural data

Journal of Computational Neuroscience

Volumn 29, Issue 1-2, 2010, Pages 107-126

  Paninski, Liam ^a   Ahmadian, Yashar ^a   Ferreira, Daniel Gil ^a   Koyama, Shinsuke ^b   Rahnama Rad, Kamiar ^a   Vidne, Michael ^a   Vogelstein, Joshua ^c   Wu, Wei ^d  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d FLORIDA STATE UNIVERSITY   (United States)

Author keywords

Hidden Markov model; Neural coding; State space models; Tridiagonal matrix

Indexed keywords

COMPUTATIONAL EFFICIENCY; DECODING; MATRIX ALGEBRA; MONTE CARLO METHODS; STATE SPACE METHODS; TRELLIS CODES;

DIRECT OPTIMIZATION; HIDDEN-MARKOV MODELS; NEURAL CODING; NEURAL DATA; NON-GAUSSIAN; OPTIMIZATION METHOD; PROPERTY; STATE-SPACE METHOD; STATE-SPACE MODELS; TRIDIAGONAL MATRICES;

HIDDEN MARKOV MODELS;

ACCURACY; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; NEUROSCIENCE; PRIORITY JOURNAL; PROBABILITY; REVIEW; SPIKE; STATISTICAL ANALYSIS; STATISTICAL MODEL; SYNAPSE;

EID: 77956895434     PISSN: 09295313     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10827-009-0179-x     Document Type: Review

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