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Open AccessArticle

A Sparse Multiwavelet-Based Generalized Laguerre–Volterra Model for Identifying Time-Varying Neural Dynamics from Spiking Activities

1
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2
Department of Mathematics, Texas A&M University at Qatar, Doha 23874, Qatar
3
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Entropy 2017, 19(8), 425; https://doi.org/10.3390/e19080425
Received: 13 July 2017 / Revised: 7 August 2017 / Accepted: 18 August 2017 / Published: 20 August 2017
(This article belongs to the Special Issue Entropy in Signal Analysis)
Modeling of a time-varying dynamical system provides insights into the functions of biological neural networks and contributes to the development of next-generation neural prostheses. In this paper, we have formulated a novel sparse multiwavelet-based generalized Laguerre–Volterra (sMGLV) modeling framework to identify the time-varying neural dynamics from multiple spike train data. First, the significant inputs are selected by using a group least absolute shrinkage and selection operator (LASSO) method, which can capture the sparsity within the neural system. Second, the multiwavelet-based basis function expansion scheme with an efficient forward orthogonal regression (FOR) algorithm aided by mutual information is utilized to rapidly capture the time-varying characteristics from the sparse model. Quantitative simulation results demonstrate that the proposed sMGLV model in this paper outperforms the initial full model and the state-of-the-art modeling methods in tracking performance for various time-varying kernels. Analyses of experimental data show that the proposed sMGLV model can capture the timing of transient changes accurately. The proposed framework will be useful to the study of how, when, and where information transmission processes across brain regions evolve in behavior. View Full-Text
Keywords: time-varying system; generalized Laguerre–Volterra model; group LASSO; b-splines basis functions; forward orthogonal regression (FOR); sparsity; spike train data time-varying system; generalized Laguerre–Volterra model; group LASSO; b-splines basis functions; forward orthogonal regression (FOR); sparsity; spike train data
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Xu, S.; Li, Y.; Huang, T.; Chan, R.H.M. A Sparse Multiwavelet-Based Generalized Laguerre–Volterra Model for Identifying Time-Varying Neural Dynamics from Spiking Activities. Entropy 2017, 19, 425.

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