Paper Info
Title
A Sparse Multiwavelet-Based Generalized Laguerre-Volterra Model for Identifying Time-Varying Neural Dynamics from Spiking Activities.
Abstract
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.
Year
DOI
Venue
2017
10.3390/e19080425
ENTROPY
Keywords
Field
DocType
time-varying system,generalized Laguerre-Volterra model,group LASSO,b-splines basis functions,forward orthogonal regression (FOR),sparsity,spike train data
Mathematical optimization,Laguerre polynomials,Spike train,Computer science,Lasso (statistics),Mutual information,Basis function,Statistics,Total least squares,Artificial neural network,Dynamical system
Journal
Volume
Issue
ISSN
19
8
1099-4300
Citations 
PageRank 
References 
0
0.34
16
Authors
4
Name
Order
Citations
PageRank
Song Xu1145.06
Yang Li211814.64
Tingwen Huang35684310.24
Rosa H M Chan418222.79