Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30174
Spike Sorting Method Using Exponential Autoregressive Modeling of Action Potentials

Authors: Sajjad Farashi


Neurons in the nervous system communicate with each other by producing electrical signals called spikes. To investigate the physiological function of nervous system it is essential to study the activity of neurons by detecting and sorting spikes in the recorded signal. In this paper a method is proposed for considering the spike sorting problem which is based on the nonlinear modeling of spikes using exponential autoregressive model. The genetic algorithm is utilized for model parameter estimation. In this regard some selected model coefficients are used as features for sorting purposes. For optimal selection of model coefficients, self-organizing feature map is used. The results show that modeling of spikes with nonlinear autoregressive model outperforms its linear counterpart. Also the extracted features based on the coefficients of exponential autoregressive model are better than wavelet based extracted features and get more compact and well-separated clusters. In the case of spikes different in small-scale structures where principal component analysis fails to get separated clouds in the feature space, the proposed method can obtain well-separated cluster which removes the necessity of applying complex classifiers.

Keywords: Exponential autoregressive model, Neural data, spike sorting, time series modeling.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1394


[1] T. I. Aksenova, O. K. Chibirova, O. A. Dryga, I. V. Tetko, A. L. Benabid, and A. E. Villa, "An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals," Methods, vol. 30, pp. 178-187, 2003.
[2] Z. Nenadic and J. W. Burdick, "Spike detection using the continuous wavelet transform," IEEE Trans Biomed Eng, vol. 52, pp. 74-87, Jan 2005.
[3] P. H. Thakur, H. Lu, S. S. Hsiao, and K. O. Johnson, "Automated optimal detection and classification of neural action potentials in extra-cellular recordings," J. Neurosci. Meth, vol. 162, pp. 364-376, 2007.
[4] M. S. Lewicki, "A review of methods for spike sorting: the detection and classification of neural action potentials," Network-Comp Neural, vol. 9, pp. 53-78, 1998.
[5] J. C. Letelier and P. P. Weber, "Spike sorting based on discrete wavelet transform coefficients," J. Neurosci. Meth, vol. 101, pp. 93-106, 2000.
[6] E. Hulata, R. Segev, Y. Shapira, M. Benveniste, and E. Ben-Jacob, "Detection and sorting of neural spikes using wavelet packets," Phys Rev Lett, vol. 85, pp. 4637-4640, Nov 20 2000.
[7] A. Pavlov, V. Makarov, I. Makarova, and F. Panetsos, "Sorting of neural spikes: When wavelet based methods outperform principal component analysis," Natural Computing, vol. 6, pp. 269-281, 2007.
[8] R. Q. Quiroga, Z. Nadasdy, and Y. Ben-Shaul, "Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering," Neural Comput, vol. 16, pp. 1661-1687, Aug 2004.
[9] D. Peel and G. J. McLachlan, "Robust mixture modelling using the t distribution," Stat Comput, vol. 10, pp. 339-348, 2000.
[10] P. M. Horton, A. U. Nicol, K. M. Kendrick, and J. F. Feng, "Spike sorting based upon machine learning algorithms (SOMA)," J. Neurosci. Meth, vol. 160, pp. 52-68, 2007.
[11] T. Hermle, C. Schwarz, and M. Bogdan, "ICA and SOM for spike sorting of multielectrode recordings from CNS," J Physiol., vol. 98, pp. 349-356, 2004.
[12] R. Prenger, M. Wu, S. V. David, and J. L. Gallant, "Nonlinear V1 Responses to Natural Scenes Revealed by Neural Network Analysis," Neural Networks, vol. 17, pp. 663-679, 2004.
[13] R. Baragona, F. Battaglia, and D. Cucina, "A note on estimating autoregressive exponential models," Quad Stat vol. 4, pp. 71–88, 2002.
[14] P. Stoica and P. Babu, "On the proper forms of BIC for model order selection," IEEE Trans Signal Process, vol. 60, pp. 4956-4961, 2012.
[15] A. Ahmadian, S. Karimifard, H. Sadoughi, and M. Abdoli, "An efficient piecewise modeling of ECG signals based on Hermitian basis functions," in Conf Proc IEEE Eng Med Biol Soc, 2007, pp. 3180-3183.
[16] T. Heskes, "Self-organizing maps, vector quantization, and mixture modeling," IEEE Transactions on Neural Networks. , vol. 12, pp. 1299-1305, 2001.