Fig. 1.

Schematic of the point process generalized linear model (PP GLM) of a recurrent spiking neuronal network. In this model, the spike trains $\overrightarrow{x}$ from the neurons in the network, after incurring transmission delays d _ij, pass through a linear filtering stage K _i. The resulting (pseudo) membrane potential U _i(t) is fed into a nonlinear link function $f_i(\cdot )=exp(\cdot )$, which transforms it into the conditional intensity function λ _i(t). The latter drives the probabilistic spiking mechanism that generates an output spike train S _i for the i-th neuron. Note that this spike train is then also fed back as an input to the neuron itself via a “self-connection” in order to model its refractory, post-spike properties