Chapter 7 - Identification of Neural Plasticity From Spikes

Abstract

Abstract This chapter describes a computational modeling approach for identifying short-term and long-term synaptic plasticity (LTSP) from spikes recorded in vivo. In this approach, synaptic strength is represented as input–output dynamics between neurons; short-term synaptic plasticity (STSP) is defined as input–output nonlinear dynamics; LTSP is formulated as the nonstationarity of such nonlinear dynamics; synaptic learning rule is essentially the function governing the characteristics of the LTSP 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 pairwise interactions between single-input spikes and single-output spikes. Using experimental and simulated input–output data, it has been shown that STSP, LTSP, and learning rules can be accurately identified with a set of nonstationary, nonlinear dynamical models.