title: Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: Computational study.
abstract: Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input-output data. Results show that the non-parametric models accurately and efficiently replicate the input-output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP.
authors.affiliation: Department of Biomedical Engineering, University of Southern California, 403 Hedco Neuroscience Building, Los Angeles, CA 90089, USA. dsong@usc.edu
authors.forename: Dong
authors.initials: D
authors.lastname: Song
authors.forename: Vasilis Z
authors.initials: VZ
authors.lastname: Marmarelis
authors.forename: Theodore W
authors.initials: TW
authors.lastname: Berger
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology*; D017711:Nonlinear Dynamics; D018709:Statistics, Nonparametric; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission