Figure 4. Probing the mechanism of divisive suppression with center-surround stimuli. ; (A) For the large spot stimulus, the Linear-Nonlinear-Kinetic (LNK) model nearly matches the performance of the DivS model, and outperforms the LN model. (B) To distinguish between different sources of divisive suppression, we presented a spot-annulus stimulus (left), where each region is independently modulated. Model filters can be extended to this stimulus using a separate temporal kernel for center and surround, shown for the LN and LNK model filters (right), which are very similar. (C) After the linear filter, the LNK model applies a nonlinearity (left), whose output drives the transition between resting and activated states (middle), which is further governed by kinetics parameters as shown. Critical kinetics parameters for LNK models differed between the large-spot and spot-annulus stimulus (right), with the spot-annulus model very quickly transitioning from Inactive back to Active states, minimizing the effects of synaptic depression. (D) The performance of the spatiotemporal LNK model is only slightly better than that of the LN model, and neither captures the details of the modulation in synaptic current, compared with the DivS model. (E) The spatiotemporal DivS model shown for an example neuron exhibits different spatial footprints for excitation and suppression, with excitation largely driven by the spot and suppression by the annulus. This divisive suppression cannot be explained exclusively by synaptic depression, which predicts overlapping sources of suppression and excitation (Figure 4—figure supplement 1 and 2). (F) The contribution of the center component in the DivS model for excitation (left) and suppression (right). Excitation was stronger in the center than in the surround (center contribution>0.5, p=0.016, n = 7) and suppression was weaker in the center (center contribution

Figure 4. Probing the mechanism of divisive suppression with center-surround stimuli. ; (A) For the large spot stimulus, the Linear-Nonlinear-Kinetic (LNK) model nearly matches the performance of the DivS model, and outperforms the LN model. (B) To distinguish between different sources of divisive suppression, we presented a spot-annulus stimulus (left), where each region is independently modulated. Model filters can be extended to this stimulus using a separate temporal kernel for center and surround, shown for the LN and LNK model filters (right), which are very similar. (C) After the linear filter, the LNK model applies a nonlinearity (left), whose output drives the transition between resting and activated states (middle), which is further governed by kinetics parameters as shown. Critical kinetics parameters for LNK models differed between the large-spot and spot-annulus stimulus (right), with the spot-annulus model very quickly transitioning from Inactive back to Active states, minimizing the effects of synaptic depression. (D) The performance of the spatiotemporal LNK model is only slightly better than that of the LN model, and neither captures the details of the modulation in synaptic current, compared with the DivS model. (E) The spatiotemporal DivS model shown for an example neuron exhibits different spatial footprints for excitation and suppression, with excitation largely driven by the spot and suppression by the annulus. This divisive suppression cannot be explained exclusively by synaptic depression, which predicts overlapping sources of suppression and excitation (Figure 4—figure supplement 1 and 2). (F) The contribution of the center component in the DivS model for excitation (left) and suppression (right). Excitation was stronger in the center than in the surround (center contribution>0.5, p=0.016, n = 7) and suppression was weaker in the center (center contribution