Fig. 5.
Model of LGN spikes based on input S-potentials and the stimulus. A: model schematic for the NIM with an added “postsynaptic current” (PSC) term p, which linearly processes the S-potential history RRGC(t) and adds to the other terms of the NIM to predict the firing rate. B: the PSC term for an example ON LGN neuron. Adding additional NIM components to the PSC term alone does influence its shape (compare red vs. green). C and D: the spike history term (C) and spiking nonlinearity (D) relative to the distribution of generating function G(t) for this neuron. Solid cyan and black dashed lines are shown to reference the magnitudes of the generator signal shown in E. E: the generator signal [summed model components for this example neuron, with (PSC-NIM, green) and without (PSC, red) an additional stimulus-processing term]. Observed S-potentials and LGN spikes are shown above the resulting model predictions. Although the effect of the additional stimulus-processing term is small relative to the fast component of the PSC term (green vs. red), it has an effect on spike probability at S-potential times and tends to decrease the probability that an S-potential will be relayed where no spikes are observed (see inset). F: the measured probability of successful S-potential transmission (blue) vs. failures (orange) for the model. G: the additional stimulus-processing term in the model of this example neuron is a suppressive OFF filter. H: OFF suppression is known as “PULL” suppression in this case, because for this neuron the S-potentials had ON selectivity but their output was also implicitly rectified. Together, this is a PUSH-PULL arrangement that approximates a full linear response.