A new Markovian approach towards neural spike sorting

Brain is the most complicated organ of body. It controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting with injured motor organ and it could be the core of functional repairing. Neurons are the vertices of a vast network that generates the brain signals. Neuronal recordings capture brain activity signatures. The processing of these signals can help to translate brain's language. Usually it follows three main stages: spike detection and extraction, spike sorting, and intention extraction from the encoded signal. In this work, we introduce an original idea based on Hidden Markov Models (HMM) which helps to improve the spike sorting stage. Our idea is a fast and simple method which uses Inter Spike Interval information besides spike waveforms to define a Hidden Markov Model that consecutive spikes should track.

[1]  Christophe Pouzat,et al.  Efficient spike-sorting of multi-state neurons using inter-spike intervals information , 2005, Journal of Neuroscience Methods.

[2]  Michael J. Berry,et al.  Refractoriness and Neural Precision , 1997, The Journal of Neuroscience.

[3]  Thomas Martinetz,et al.  Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations , 2005, Neurocomputing.

[4]  Zoubin Ghahramani,et al.  An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..

[5]  Byron M. Yu,et al.  Detecting neural-state transitions using hidden Markov models for motor cortical prostheses. , 2008, Journal of neurophysiology.

[6]  J. Csicsvari,et al.  Intracellular features predicted by extracellular recordings in the hippocampus in vivo. , 2000, Journal of neurophysiology.

[7]  Kyung Hwan Kim.  A Fully-Automated Neural Spike Sorting Based on Projection Pursuit and Gaussian Mixture Model , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[8]  You-Yin Chen,et al.  Dictionary-Based Classification Models. Applications for Multichannel Neural Activity Analysis , 2009, EANN.

[9]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[10]  Sung June Kim,et al.  Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio , 2003, IEEE Transactions on Biomedical Engineering.

[11]  E. Fetz,et al.  Direct control of paralyzed muscles by cortical neurons , 2008, Nature.

[12]  Bernadette Dorizzi,et al.  ECG signal analysis through hidden Markov models , 2006, IEEE Transactions on Biomedical Engineering.

[13]  Markus Stengel,et al.  Introduction to Graphical Models, Hidden Markov Models and Bayesian Networks , 2003 .

[14]  R. Quian Quiroga,et al.  Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering , 2004, Neural Computation.

[15]  Joel W. Burdick,et al.  A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals , 2009, IEEE Transactions on Biomedical Engineering.

[16]  Lalit M. Patnaik,et al.  Epileptic EEG detection using neural networks and post-classification , 2008, Comput. Methods Programs Biomed..

[17]  M. Nechyba,et al.  HMM-BASED CLASSIFICATION OF MULTI-CHANNEL NEURAL SPIKE DATA , 2002 .

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

[19]  Daniel G. Keehn,et al.  An Iterative Spike Separation Technique , 1966 .

[20]  Christophe Pouzat,et al.  Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach. , 2004, Journal of neurophysiology.

[21]  Kevin P. Murphy,et al.  An introduction to graphical models , 2011 .

[22]  Susumu Takahashi,et al.  Classification of neuronal activities from tetrode recordings using independent component analysis , 2002, Neurocomputing.