A neuron signature based spike feature extraction algorithm for on-chip implementation.

A spike derivative based feature extraction algorithm is reported in this paper. The theoretical framework includes neuronal geometry signatures and noise shaping. Through evaluating neuronal geometry signatures with compartment model, we obtain improved differentiation among similar spikes by emphasizing high frequency signal spectrum. Through studying the noise properties, we find the total noise reduces by taking the derivative of spikes, which is the simplest frequency shaping filter boosting the high frequency signal spectrum. In addition, a preliminary hardware implementation to extract spike features has been realized using an integrated microchip interfaced with a personal computer.

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

[2]  G. Buzsáki,et al.  Pattern and inhibition-dependent invasion of pyramidal cell dendrites by fast spikes in the hippocampus in vivo. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[3]  K.V. Shenoy,et al.  Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Christof Koch,et al.  Electrical Interactions via the Extracellular Potential Near Cell Bodies , 1999, Journal of Computational Neuroscience.

[5]  Taejeong Kim,et al.  Solving alignment problems in neural spike sorting using frequency domain PCA , 2006, Neurocomputing.

[6]  John P. Cunningham,et al.  Increasing the Performance of Cortically-Controlled Prostheses , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Michael S. Lewicki,et al.  Bayesian Modeling and Classification of Neural Signals , 1993, Neural Computation.

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

[9]  Yi Zhou,et al.  Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem , 2004, Journal of Neuroscience Methods.

[10]  P. M. Horton,et al.  Spike sorting based upon machine learning algorithms (SOMA) , 2007, Journal of Neuroscience Methods.

[11]  G. Buzsáki.  Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

[12]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[13]  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.