An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings

Network: Computation in Neural Systems

Volumn 14, Issue 1, 2003, Pages 61-82

  Nguyen, David P ^a,b   Frank, Loren M ^a   Brown, Emery N ^a  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ANIMAL; ANISOTROPY; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER SIMULATION; CONFERENCE PAPER; DATA ANALYSIS; ELECTROPHYSIOLOGY; ENTORHINAL CORTEX; EXCITATORY JUNCTION POTENTIAL; EXTRACELLULAR RECORDING; METHODOLOGY; MONTE CARLO METHOD; NERVE CELL; NERVE FIBER MEMBRANE POTENTIAL; NONHUMAN; NORMAL DISTRIBUTION; PHYSIOLOGY; PROBABILITY; PROCEDURES CONCERNING CELLS; RAT; SIMULATION; THEORETICAL MODEL;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTER SIMULATION; ELECTROPHYSIOLOGY; MARKOV CHAINS; METHODS; MODELS, NEUROLOGICAL; MONTE CARLO METHOD; NEURONS; PHYSIOLOGY; RATS;

EID: 0348147518     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X/14/1/304     Document Type: Conference Paper

References (27)

1. Andersen T W 1984 An Introduction to Multivariate Statistical Analysis (New York: Wiley) pp 244-79
2. Bierer S M and Anderson D J 1999 Multi-channel spike detection and sorting using an array processing technique Neurocomputing 26/27 947-56
3. Diebolt J and Robert C P 1994 Estimation of finite mixture distributions through Bayesian sampling J. R. Stat. Soc. 56 363-75
4. Doucet A, de Freitas N and Gordon N 2001 Sequential Monte Carlo in Practice (New York: Springer)
5. Escobar M D and West M 1995 Bayesian density estimation and inference using mixtures J. Am. Stat. Assoc. 90 577-88
6. Fee M S, Mitra P P and Kleinfeld D 1996 Automatic sorting of multiple neuronal signals in the presence of anisotropic and non-Guassian variability J. Neurosci. Methods 69 175-88
7. Frank L M, Brown E N and Wilson M A 2000 Trajectory encoding in the hippocampus and entorhinal cortex Neuron 27 169-78
8. Gelfand A E, Hills S E, Racine-Poon A and Smith A F M 1990 Illustration of Bayesian inference in normal data models using Gibbs sampling J. Am. Stat. Assoc. 85 972-85
9. Gray C M, Maldonado P E, Wilson M and McNaughton B 1995 Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex J. Neurosci. Methods 63 43-54
10. Green P 1995 Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Biometrika 82 711-32
11. Hastings W K 1970 Monte Carlo sampling methods using Markov chains and their applications Biometrika 57 97-109
12. Kotz S, Balakrishnan N and Johnson N L 2000 Continuous Multivariate Distributions vol 1, 2nd edn (New York: Wiley) pp 485-91
13. Lavine M and West M 1992 A Bayesian method for classification and discrimination Can. J. Stat. 20 451-61
14. Lewicki M S 1994 Bayesian modeling and classification of neural signals Neural Comput. 6 1005-30
15. Lewicki M S 1998 A review of methods for spike sorting: the detection and classification of neural action potentials Network: Comput. Neural Syst. 9 R53-78
16. Lutkepohl H 1996 Handbook of Matrices (New York: Wiley)
17. Magnus J R and Neudecker H 1999 Matrix Differential Calculus with Applications in Statistics and Econometrics 2nd edn (New York: Wiley)
18. McLachlan G J and Krishnan T 1997 The EM Algorithm and Extensions (New York: Wiley)
19. Metropolis N, Rosenbluth A W, Rosenbluth M N, Teller A H and Teller E 1953 Equations of state calculations by fast computing machines J. Chem. Phys. 21 1087-91
20. Plonsey R and Barr R C 2000 Bioelectricity: A Quantitative Approach 2nd edn (Boston, MA: Kluwer)
21. Richardson S and Green P 1997 On Bayesian analysis of mixtures with an unknown number of components J. R. Stat. Soc. 59 731-92
22. Robert C P 1996 Mixtures of distributions: inference and estimation Markov chain Monte Carlo in Practice vol 24, ed W R Gilks, S Richardson and D J Spiegelhalter (New York: Chapman and Hall) pp 441-64
23. Robert C P and Casella G 1999 Monte Carlo Statistical Methods (New York: Springer)
24. Sahani M, Pezaris J S and Andersen R A 1997 On the separation of signals from neighboring cells in tetrode recordings Neural Information Processing Systems (NIPS 1997) vol 10, ed M Jordan, M Kearns and S Solla (Cambridge, MA: MIT Press) p 11
25. Shoham S 2001 Advances towards an implantable motor cortical interface PhD Thesis University of Utah, Salt Lake City, UT
26. Storvik G 2002 Particle filter for state-space models with the presence of unknown static parameters IEEE Trans. Sig. Process. 50 281-9
27. Tenenbaum J B, de Silva V and Langford J C 2000 A global geometric framework for nonlinear dimensionality reduction Science 2319-23