Analysis of spike-driven processes through attributable components

Postsynaptic neuron activity at both the sub and suprathreshold level is analyzed through the combination of: (1) the numerical simulation of a simple leaky integrate-and-fire model forced by both constant frequency and Poisson-distributed presynaptic spike-trains,(2) the transformation of the model's response into sequences describing non-summation effects in subthreshold and the probability of spiking within a time-window in suprathreshold dynamics, (3) for constant frequency input, the analysis of these sequences through an autoregressive linear model, and (4) for non-uniform input, their analysis through attributable components. It is found that the attributable component methodology can reproduce the dynamics on testing data, effectively replacing the original dynamical model, and that the optimal order of both the autoregressive and the attributable component model, is an indicator of the relative strength of the underlying depression and facilitation mechanisms.