Abstract
Fast and reliable unsupervised spike sorting is necessary for electrophysiological applications that require critical time operations (e.g., recordings during human neurosurgery) or management of large amount of data (e.g., recordings from large microelectrode arrays in behaving animals). We present an algorithm that can recognize the waveform of neural traces corresponding to extracellular action potentials. Spike shapes are expressed in a phase space spanned by the first and second derivatives of the raw signal trace. The performance of the algorithm is tested against artificially generated noisy data sets. We present the main features of the algorithm aimed to on-line real-time operations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aksenova, T.I., Chibirova, O., Dryga, A.O., Tetko, I.V., Benabid, A., Villa, A.E.P.: An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animal. Methods 30, 178–187 (2003)
Benabid, A.L., Pollak, P., Gross, C., Hoffmann, D., Benazzouz, A., Gao, D.M., Laurent, A., Gentil, M., Perret, J.: Acute and long-term effects of subthalamic nucleus stimulation in Parkinson’s disease. Stereotact. Funct. Neurosurg. 62, 76–84 (1994)
Chibirova, O.K., Aksenova, T.I., Benabid, A.-L., Chabardes, S., Larouche, S., Rouat, J., Villa, A.E.P.: Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients. BioSystems 79, 159–171 (2005)
Letelier, J.C., Weber, P.P.: Spike sorting based on discrete wavelet transform coefficients. Journal of Neuroscience Methods 101, 93–106 (2000)
Lewicki, M.S.: Bayesian modeling and classification of neural signals. Neural Comp. 6, 1005–1030 (1994)
Takahashi, S., Anzai, Y., Sakurai, Y.: A new approach to spike sorting for multi-neuronal activities recorded with a tetrode – how ICA can be practical. Neuroscience Research 46, 265–272 (2003)
Villa, A.E.P., Tetko, I.V., Hyland, B., Najem, A.: Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task. Proc. Natl. Acad. Sci. USA 96, 1006–1011 (1999)
Zhang, P.M., Wu, J.Y., Zhoub, Y., Liang, P.J., Yuan, J.Q.: Spike sorting; Template-matching; χ 2-Test; Overlapping; Principal component analysis; Subtractive clustering. Journal of Neuroscience Methods 135, 55–65 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Asai, Y., Aksenova, T.I., Villa, A.E.P. (2005). Unsupervised Recognition of Neuronal Discharge Waveforms for On-line Real-Time Operation. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds) Brain, Vision, and Artificial Intelligence. BVAI 2005. Lecture Notes in Computer Science, vol 3704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11565123_3
Download citation
DOI: https://doi.org/10.1007/11565123_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29282-1
Online ISBN: 978-3-540-32029-6
eBook Packages: Computer ScienceComputer Science (R0)