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 Univerity   (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

References (19)

1. K. H. Kim and S. J. Kim, "Neural spike sorting under nearly 0-db signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier," IEEE Trans. on Biomedical Engineering, vol. 47, pp. 1406-1411, 2000.
2. S. Takahashi, Y. Anzai, and Y. Sakurai, "Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes," Journal of Neurophysiology, vol. 89, pp. 2245-2258, 2003.
3. S. Shoham, M. R. Fellows, and R. A. Normann, "Robust, automatic spike sorting using mixtures of multivariate t-distributions," Journal of Neuroscience Methods, vol. 127(2), pp. 111-122, 2003.
4. E. Hulata, R. Segev, and E. Ben-Jacob, "A method for spike sorting and detection based on wavelet packets and Shannon's mutual information," Journal of Neuroscience Methods, vol. 117, pp. 1-12, 2002.
5. M. S. Lewicki, "A review of methods for spike sorting: the detection and classification of neural action potentials," Network, vol. 9, no. 4, pp. R53-78, 1998.
6. F. Wood, M. Fellows, J. P. Donoghue, and M. J. Black, "Automatic spike sorting for neural decoding," in IEEE Engineering Medicine Biological Systems, 2004, pp. 4126-4129.
7. M. Sahani, J. S. Pezaris, and R. A. Andersen, "On the separation of signals from neighboring cells in tetrode recordings," in Advances in Neural Information Processing Systems 10. MIT Press, 1998.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, B, vol. 39, 1977.
9. N. Friedman, "The Bayesian structural EM algorithm," in Proceedings of the 14th Annual conference on uncertainty in AI, 1998, pp. 129-138.
10. P. Green, "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination," Biometrika, pp. 711-732, 1995.
11. C. Rasmussen, "The infinite Gaussian mixture model," in Advances in Neural Information Processing Systems 12. Cambridge, MA: MIT Press, 2000.
12. R. M. Neal, "Probabilistic inference using Markov chain Monte Carlo methods," University of Toronto, Tech. Rep. CRG-TR-93-1, 1993.
13. _, "Markov chain sampling methods for Dirichlet process mixture models," Department of Statistics, University of Toronto, Tech. Rep. 9815, 1998.
14. S. MacEachern and P. Müller, "Estimating mixture of Dirichlet process models," Journal of Computational and Graphical Statistics, vol. 7, pp. 223-238, 1998.
15. T. L. Griffiths and Z. Ghahramani, "Infinite latent feature models and the Indian buffet process," Gatsby Computational Neuroscience Unit, Tech. Rep. 2005-001, 2005.
16. M. D. Escobar and M. West, "Bayesian density estimation and inference using mixtures," Journal of the American Statistical Association, vol. 90, pp. 577-588, 1995.
17. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian data analysis. New York: Chapman & Hall, 1995.
18. C. Fraley and A. E. Raftery, "Bayesian regularization for normal mixture estimation and model-based clustering," Department of Statistics, Washington University, Seattle, Washington, Tech. Rep. 05/486, 2005.
19. R. M. Neal, "Markov chain sampling methods for Dirichlet process mixture models," Journal of Computational and Graphical Statistics, vol. 9, pp. 249-265, 2000.