Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)


For citation:

Tupitsyn A. N., Nazimov A. I., Pavlov A. N. Identification of Action Potentials of Small Neuron Ensembles Using Wavelet-Analysis and Neural Networks Method. Izvestiya of Saratov University. Physics , 2009, vol. 9, iss. 2, pp. 57-65. DOI: 10.18500/1817-3020-2009-9-2-57-65

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text:
(downloads: 140)
Language: 
Russian
Heading: 
UDC: 
537.86:519.2

Identification of Action Potentials of Small Neuron Ensembles Using Wavelet-Analysis and Neural Networks Method

Autors: 
Tupitsyn Anatoly Nikolaevich, Saratov State University
Nazimov Alexei Igorevich, Saratov State University
Pavlov Alexyi Nikolaevich, Saratov State University
Abstract: 

A possibility to solve the problem of automatic identification of neuronal spikes in the extracellularly recorded electrical potentials is discussed that is based on a combined approach assuming application of neural networks and the discrete wavelet-transform. Efficiency of the combined approach is illustrated in the analysis of experimental data.

Reference: 

1. Tuckwell Н.С. Introduction to theoretical neurobiology Cambridge: Cambridge University Press, 1988.

2. Lewicki M. A review of methods for spike sorting: the detection and classification of neural potencials // Net. Com. Neu. Sys. 1998. Vol.9. P.R53-R78.

3. Letelier J., Weber P. Spike sorting based on discrete wavelet transform coefficients // J. of Neuroscience Methods. 2000. Vol.101. P.93-106.

4. Hulata E., Segev R., Ben-Jacob E. A metod for spike sorting and detection based on wavelet packets and Shannon's mutual information // J. of Neuroscience Methods. 2002. Vol.117. P.l-12.

5. Daubechies I. Ten lectures on wavelets. Philadelphia: S.I.A.M., 1992.

6. Meyer Y. Wavelets: Algorithms and applications. Philadelphia: S.I.A.M., 1993.

7. Addison P.S. The illustrated wavelet transform handbook: applications in science, engineering, medicine and finance. Philadelphia: IOP Publishing, 2002.

8. Думский Д.В., Павлов A.M., Тупицын А.Н., Макаров В.А. Классификация нейронных потенциалов действия на основе вейвлет-преобразования // Изв. вузов. Прикладная нелинейная динамика. 2005. 1'. 13, №5-6. С.77-98.

9. Pavlov A.N., Makarov V.A., Makarova L, Panetsos F. Separation of extracellular spikes: when wavelet based methods outperfonn the principle component analysis // Lecture Notes in Computer Science / Ms. j . Mira, J.R. Alvarez. Berlin, 2005. P.123-132.

10. Pavlov A.N., Makarov V.A., Makarova L, Panetsos F. Sorting of extracellular spikes: When wavelet based methods outperform the principle component analysis // Natural Computing. 2007. Vol.6. P.269-281.

11. Макаров В.А., Павлов А.Н., Тупицын A.M. Сортировка нейронных спайков на основе параметрического вейвлет-анализа с адаптивной фильтрацией // Цифровая обработка сигналов. 2008. №3. С.26-31.

12. Haykin S. Neural networks. A comprehensive foundation. New Jersey: Prentice Hall, 1999.

13. Kohonen T. Selforganization and associative memory. N.Y.: Springer-Verlag, 1989.

14. Ihpfield J., Tank D. Neural computation of decision in optimization problems // Biol. Cybernet 1985. Vol.52. Р.14Ы52.

15. Callan R. The essence of neural networks. New Jersey: Prentice Hall, 1999.