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Assessing Complexity of Neuronal Multiunit Activity by Information Theoretic Measure

Authors: Young-Seok Choi


This paper provides a quantitative measure of the time-varying multiunit neuronal spiking activity using an entropy based approach. To verify the status embedded in the neuronal activity of a population of neurons, the discrete wavelet transform (DWT) is used to isolate the inherent spiking activity of MUA. Due to the de-correlating property of DWT, the spiking activity would be preserved while reducing the non-spiking component. By evaluating the entropy of the wavelet coefficients of the de-noised MUA, a multiresolution Shannon entropy (MRSE) of the MUA signal is developed. The proposed entropy was tested in the analysis of both simulated noisy MUA and actual MUA recorded from cortex in rodent model. Simulation and experimental results demonstrate that the dynamics of a population can be quantified by using the proposed entropy.

Keywords: Discrete wavelet transform, Entropy, Multiresolution, Multiunit activity.

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[1] G. Buzsaki, "Large-scale recording of neuronal ensembles," Nat Neurosci, vol. 7, pp. 446-51, May 2004.
[2] I. Nemenman, W. Bialek, and R. de Ruyter van Steveninck, "Entropy and information in neural spike trains: progress on the sampling problem," Phys Rev E Stat Nonlin Soft Matter Phys, vol. 69, p. 056111, May 2004.
[3] K. H. Pettersen, E. Hagen, and G. T. Einevoll, "Estimation of population firing rates and current source densities from laminar electrode recordings," J Comput Neurosci, vol. 24, pp. 291-313, Jun 2008.
[4] M. E. Nelson, "Multiscale spike train variability in primary electrosensory afferents," J Physiol Paris, vol. 96, pp. 507-16, Sep-Dec 2002.
[5] S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 11, pp. 674-693, 1989.
[6] R. J. Brychta, S. Tuntrakool, M. Appalsamy, N. R. Keller, D. Robertson, R. G. Shiavi, et al., "Wavelet methods for spike detection in mouse renal sympathetic nerve activity," IEEE Trans Biomed Eng, vol. 54, pp. 82-93, Jan 2007.
[7] P. M. Zhang, J. Y. Wu, Y. Zhou, P. J. Liang, and J. Q. Yuan, "Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem," J Neurosci Methods, vol. 135, pp. 55-65, May 30 2004.
[8] O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, et al., "Wavelet entropy: a new tool for analysis of short duration brain electrical signals," Journal of neuroscience methods, vol. 105, pp. 65-75, 2001.