title: Sparse generalized Laguerre-Volterra model of neural population dynamics.
abstract: To understand the function of a brain region, e.g., hippocampus, it is necessary to model its input-output property. Such a model can serve as the computational basis of the development of cortical prostheses restoring the transformation of population neural activities performed by the brain region. We formulate a sparse generalized Laguerre-Volterra model (SGLVM) for the multiple-input, multiple-output (MIMO) transformation of spike trains. A SGLVM consists of a set of feedforward Laguerre-Volterra kernels, a feedback Laguerre-Volterra kernel, and a probit link function. The sparse model representation involving only significant self and cross terms is achieved through statistical model selection and cross-validation methods. The SGLVM is applied successfully to the hippocampal CA3-CA1 population dynamics.
authors: Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089, USA. dsong@usc.edu
authors: Dong
authors: D
authors: Song
authors: Rosa H M
authors: RH
authors: Chan
authors: Vasilis Z
authors: VZ
authors: Marmarelis
authors: Robert E
authors: RE
authors: Hampson
authors: Sam A
authors: SA
authors: Deadwyler
authors: Theodore W
authors: TW
authors: Berger
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D056547:CA1 Region, Hippocampal / Q000502:physiology; D056654:CA3 Region, Hippocampal / Q000502:physiology; D006624:Hippocampus / Q000502:physiology*; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D051381:Rats; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric