A two-stage cascade nonlinear dynamical model of single neurons for the separation and quantification of pre- and post-synaptic mechanisms of synaptic transmission

Neurons receive pre-synaptic spike trains and transform them into post-synaptic spike trains. This spike train to spike train temporal transformation underlies all cognitive functions performed by neurons, e.g., learning and memory. The transformation is a highly nonlinear dynamical process that involves both pre- and post-synaptic mechanisms. The ability to separate and quantify the nonlinear dynamics of pre- and post-synaptic mechanism is needed to gain insights into this transformation. In this study, we developed a Volterra kernel based two-stage cascade model of synaptic transmission using synaptically-driven intracellular activities, to which broadband stimulation conditions were imposed. The first stage of the model represents the pre-synaptic mechanisms and describes the nonlinear dynamical transformation from pre-synaptic spike trains to transmitter vesicle release strengths. The vesicle release strengths were obtained from the intracellularly recorded excitatory post-synaptic currents (EPSCs). The second stage of the model represents the post-synaptic mechanisms and describes the nonlinear dynamical transformation from release strengths to excitatory post-synaptic potentials (EPSPs). One application of this cascade model is to analyze the pre- and post-synaptic mechanism change induced by long-term potentiation (LTP). This future application is expected to shed new light on the expression locus of LTP.