Sparse generalized Laguerre-Volterra model of neural population dynamics.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

View graph of relations

Author(s)

  • Dong Song
  • Vasilis Z Marmarelis
  • Robert E Hampson
  • Sam A Deadwyler
  • Theodore W Berger

Detail(s)

Original languageEnglish
Pages (from-to)4555-4558
Journal / PublicationConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Publication statusPublished - 2009
Externally publishedYes

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.

Citation Format(s)

Sparse generalized Laguerre-Volterra model of neural population dynamics. / Song, Dong; Chan, Rosa H M; Marmarelis, Vasilis Z; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W.

In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2009, p. 4555-4558.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal