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

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... stimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input-output data. Results show that the nonparametric models accurately and efficiently replicate the input-output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP.