Chapter 13 - Identification of Short-Term and Long-Term Functional Synaptic Plasticity From Spiking Activities

Abstract

This chapter describes a nonstationary nonlinear dynamical modeling approach for identifying short-term and long-term synaptic plasticity from point-process spiking activities. In this approach, synaptic strength is represented as input–output dynamics between neurons; short-term synaptic plasticity is defined as input–output nonlinear dynamics; long-term synaptic plasticity is formulated as the nonstationarity of such nonlinear dynamics; the synaptic learning rule is essentially the function governing the characteristics of the long-term synaptic plasticity based on the input–output spiking patterns. As a special case, spike timing–dependent plasticity is equivalent to a second-order learning rule describing the pair-wise interactions between single input spikes and single output spikes. Using experimental and simulated input–output data, it has been shown that short-term synaptic plasticity, long-term synaptic plasticity and learning rule can be accurately identified with a set of nonstationary nonlinear dynamical models.