Abstract
Spectral methods allow the estimation of the firing frequency in the activity of a single neuron. However, transient periods, changes in the neuron firing frequency or even changes in the neuron activity regime (rest, tonic firing or spiking) due to different inputs or to the presence of neurotransmitters are not well detected by means of these methods due to the fact that frequency and time are not commutable operators. Some other methods have been developed to deal with local transients, for example the localized Fourier transform or the Wigner distribution. Unfortunately these localized methods need fine tuning to find an adequate working resolution and the resulting coefficients are hard to interpret. In this work we propose the use of the tomographic transforms to detect and characterize transient components in the behaviour of a single neuron.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Szucs, A., Varona, P., Volkovskii, A.R., Abarbanel, H.D.I., Rabinovich, M.I., Selverston, A.I.: Interacting biological and electronic neurons generate realistic oscillatory rhythms. Neuro Report 11(3), 563–569 (2000)
Aguirre, C., Pascual, P.: A Wavelet Based Method for Detecting Multiple Encoding Rhythms in Neural Networks. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 9–16. Springer, Heidelberg (2009)
Cohen, L.: Time-frequency distributions - A review. Proc. IEEE 77, 941–981 (1989)
Khadra, L.M.: Time-frequency distribution of multi-component signals. Int. J. of Electronics 67, 53–57 (1989)
Choi, H.I., Williams, W.J.: Improved time-frequency representation of multi-component signals using exponential kernels. IEEE Trans. Acoust. Speech Signal Process 37, 862–871 (1989)
Fineberg, A.B., Mammone, R.J.: Detection and classification of multicomponent signals. In: Proceedings of 25th Asilomar Conference on Computer Signal and Systems, pp. 1093–1097 (1991)
Briolle, F., Lima, R., Manko, V.I., Vilela Mendes, R.: A tomographic analysis of reflectometry data I: Component factorization. Meas. Sci. Technol. 20(10), 105501–105511 (2009)
Briolle, F., Lima, R., Manko, V.I., Vilela Mendes, R.: A tomographic analysis of reflectometry data II: phase derivative. Meas. Sci. Technol. 20(10), 105512–105522 (2009)
Aguirre, C., Campos, D., Pascual, P., Serrano, E.: Neuronal behavior with sub-threshold oscillations and spiking/bursting activity using a piecewise linear two-dimensional map. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 103–108. Springer, Heidelberg (2005)
Manko, M.A., Manko, V.I., Vilela Mendes, R.: Tomograms and other transforms: a unified view. Journal of Physics A: Math. Gen. 34, 8321–8332 (2001)
Manko, V.I., Vilela Mendes, R.: Noncommutative time frequency tomography. Physics Letters A 263, 53–59 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aguirre, C., Pascual, P., Campos, D., Serrano, E. (2011). Single Neuron Transient Activity Detection by Means of Tomography. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-21501-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21500-1
Online ISBN: 978-3-642-21501-8
eBook Packages: Computer ScienceComputer Science (R0)