Abstract
•Formulation of a nonstationary nonlinear dynamical modeling approach for the identification of long-term synaptic plasticity using natural spiking activities.•Elucidation of the relationship between Volterra kernels and spike-timing-dependent plasticity functions.•Test of nonstationary modeling and learning rule identification methods with simulated spiking input–output data.
This paper presents a systems identification approach for studying the long-term synaptic plasticity using natural spiking activities. This approach consists of three modeling steps. First, a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MISO model is extended to a nonstationary form to track the time-varying properties of the synaptic strength. Finally, a Volterra modeling method is used to extract the synaptic learning rule, e.g., spike-timing-dependent plasticity, for the explanation of the input–output nonstationarity as the consequence of the past input–output spiking patterns. This framework is developed to study the underlying mechanisms of learning and memory formation in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.
