Sensitizing cells increased their high-contrast response by 61% ± 17% in 100% contrast compared to 35% contrast. They also increased their steady-state low-contrast response by 153% ± 51% in 7% contrast compared to 5% contrast. Even with a firing rate higher than in picrotoxin, sensitizing cells continued to sensitize under the higher contrast condition, as the adaptive index was 0.36 ± 0.06 for 35% to 5% contrast, Ruxolitinib concentration and 0.21 ± 0.01 for 100% to 7% contrast

(Figure S4C). Here, we have studied multiple aspects of how adaptation and sensitization combine in single ganglion cells. As to the general phenomenon, fast Off ganglion cells have center-surround AF, showing central adaptation but peripheral sensitization (Figure 1). Furthermore, spatial antagonism of plasticity occurs at a subcellular scale (Figure 3), and sensitization occurs in a selleck screening library rapidly changing contrast environment (Figure 4). As to the computation, a model with independently adapting excitatory and inhibitory subunits explains spatiotemporal plasticity within the AF (Figures 2, 3, and 4). The model further shows that varying inhibitory strength can generate the different

AFs. As to the underlying mechanisms, a membrane potential depolarization underlay sensitization of the firing rate (Figure S3B). Sensitization also requires GABAergic inhibition but not transmission through GABAA receptors (Figure 8). Certain bipolar cells depolarize following high contrast and connect to ganglion cells that show sensitization (Figure 9). Furthermore, partial blockade of GABAergic transmission supports the idea that different levels of inhibition produce different types of AF. As to the

functional relevance of sensitization, OMS cells have a center-surround AF and act as feature detectors (Figure 5). Fast Off sensitizing cells, although not OMS cells, have a similarly sharp threshold and respond to the same local features as fast unless Off adapting cells (Kastner and Baccus, 2011). Finally, as to a theoretical understanding of these results, the sensitizing effect on nonlinearities is consistent with a simple model showing that inhibition acts as a bias in the detection of an effective stimulus (Figure 6). Furthermore, the spatiotemporal sensitizing field conforms to a recursive inference model that updates the prior probability of a signal, predicting a sensitizing surround larger than the immediate response. Testing this idea with a stimulus representing a camouflaged object, we showed that sensitization enables the prediction of an object’s future position (Figure 7). Even though the classical receptive field (Barlow, 1953 and Kuffler, 1953) incompletely describes the response of a cell, part of its usefulness comes from the fact that, to some extent, different spatial regions provide independent contributions to the response of the cell.