During selleck chemical Learly, sensitization displays all three properties expected from an ideal model of signal detection: decreased threshold, increased baseline, and decreased slope. Thus, changes in the response curve during sensitization parallel an ideal model of signal detection when the probability of the signal increases. We then quantitatively compared the output of the

optimal model to the change in firing rate seen in the nonlinearities from L  early and L  late. Low values of input should yield near-zero firing rate in ganglion cells, owing to the apparent pressure to convey information about the stimulus using few spikes ( Pitkow and Meister, 2012). To convert the prior probability, p(s|ν)p(s|ν), to a firing rate, we used a nonlinearity, Np(p(s|ν))Np(p(s|ν)) ( Figure 6B), optimized to map p(s|ν)p(s|ν) to the firing rate averaged over all cells during both L  early and L  late conditions; i.e., only a single function was used for all cells and all conditions. This function had a sharp threshold corresponding to approximately a p(s|ν)p(s|ν) of ∼0.5. Thus, a comparison of ganglion cell firing with the optimal signal detection model allowed

us to interpret that the cell fired when it was more likely than not that a signal was present. We then examined how closely the model matched the nonlinearity during Learly. Although the signal detection model was not optimized to account for any difference between Learly and Llate, it predicted the magnitude

of the change in both midpoint and slope of the nonlinearity between Learly and Llate ( Figures 6D and 6E). In the Liver X Receptor agonist signal detection model, the time course that the signal probability increased was faster than when it decayed, differing by a factor of 3 (Figure 6C). This temporal asymmetry reflects that it is easier to detect an increase in contrast than a decrease in contrast, because an increase in contrast quickly brings extreme intensity values inconsistent with the previous low contrast (DeWeese and Zador, 1998). This asymmetry corresponded to our measurements, as sensitization decayed with a tau 4.4 times longer than sensitization developed—2.4 s versus 0.55 s (Kastner and Baccus, 2011). Therefore, both qualitatively and quantitatively, sensitization within the AF also conforms to an optimal model of signal detection in the presence of background noise. We thus propose that the sensitizing field provides a bias for the detection of a signal based on the prior probability of that signal, conditioned on the stimulus history. We tested this idea in a more natural context relating to the motion of objects, which represents an important source of visual signals. In a natural environment, objects do not suddenly disappear; therefore, once detected, they are highly likely to remain nearby in space.