title: Statistical selection of multiple-input multiple-output nonlinear dynamic models of spike train transformation.
abstract: Multiple-input multiple-output nonlinear dynamic model of spike train to spike train transformations was previously formulated for hippocampal-cortical prostheses. This paper further described the statistical methods of selecting significant inputs (self-terms) and interactions between inputs (cross-terms) of this Volterra kernel-based model. In our approach, model structure was determined by progressively adding self-terms and cross-terms using a forward stepwise model selection technique. Model coefficients were then pruned based on Wald test. Results showed that the reduced kernel models, which contained much fewer coefficients than the full Volterra kernel model, gave good fits to the novel data. These models could be used to analyze the functional interactions between neurons during behavior.
authors.affiliation: Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089 USA. dsong@usc.edu
authors.forename: Dong
authors.initials: D
authors.lastname: Song
authors.forename: Rosa H M
authors.initials: RH
authors.lastname: Chan
authors.forename: Vasilis Z
authors.initials: VZ
authors.lastname: Marmarelis
authors.forename: Robert E
authors.initials: RE
authors.lastname: Hampson
authors.forename: Sam A
authors.initials: SA
authors.lastname: Deadwyler
authors.forename: Theodore W
authors.initials: TW
authors.lastname: Berger
mesh_terms: D000818:Animals; D001519:Behavior / Q000502:physiology; D001921:Brain / Q000502:physiology*; D004558:Electric Stimulation; D016013:Likelihood Functions; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D051381:Rats