A Non-Parametric Bayesian Approach to Spike Sorting

In this work we present and apply infinite Gaussian mixture modeling, a non-parametric Bayesian method, to the problem of spike sorting. As this approach is Bayesian, it allows us to integrate prior knowledge about the problem in a principled way. Because it is non-parametric we are able to avoid model selection, a difficult problem that most current spike sorting methods do not address. We compare this approach to using penalized log likelihood to select the best from multiple finite mixture models trained by expectation maximization. We show favorable offline sorting results on real data and discuss ways to extend our model to online applications

[1]  Yoshio Sakurai,et al.  Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. , 2003, Journal of neurophysiology.

[2]  Thomas L. Griffiths,et al.  Infinite latent feature models and the Indian buffet process , 2005, NIPS.

[3]  Radford M. Neal.  Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .

[4]  M S Lewicki,et al.  A review of methods for spike sorting: the detection and classification of neural action potentials. , 1998, Network.

[5]  Carl E. Rasmussen,et al.  The Infinite Gaussian Mixture Model , 1999, NIPS.

[6]  M. Escobar,et al.  Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .

[7]  Adrian E. Raftery,et al.  Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering , 2007, J. Classif..

[8]  M. Escobar,et al.  Bayesian Density Estimation and Inference Using Mixtures , 1995 .

[9]  Sung June Kim,et al.  Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier , 2000, IEEE Transactions on Biomedical Engineering.

[10]  Richard A. Andersen,et al.  On the Separation of Signals from Neighboring Cells in Tetrode Recordings , 1997, NIPS.

[11]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[12]  S. MacEachern,et al.  Estimating mixture of dirichlet process models , 1998 .

[13]  R. Segev,et al.  A method for spike sorting and detection based on wavelet packets and Shannon's mutual information , 2002, Journal of Neuroscience Methods.

[14]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[15]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[16]  Shy Shoham,et al.  Robust, automatic spike sorting using mixtures of multivariate t-distributions , 2003, Journal of Neuroscience Methods.

[17]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[18]  Michael J. Black,et al.  Automatic spike sorting for neural decoding , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.