Thus, one would expect that most luminance adaptation occurs at an earlier stage, whereas contrast adaptation would occur only after the beta-catenin assay threshold

nonlinearity. However, at lower luminance the bipolar cell filter is more monophasic, transmitting more information about the mean luminance (Burkhardt et al., 2007). Accordingly, at low intensities, the bipolar cell terminal in the primate cone pathway does adapt to the mean luminance (Dunn et al., 2007). Because our results indicate that contrast adaptation is based on the mean signal at the bipolar cell terminal, adaptation to the mean luminance and contrast of the stimulus may not be independent at lower luminance. In principle, adaptive changes can be produced by one parallel pathway that modulates a second pathway (Mante et al., 2008 and Cook and McReynolds, 1998). Parallel pathways have flexibility, in that stimuli that cause adaptation can differ from those encoded by the immediate response of the cell. This organization, however, requires additional neural circuitry to generate adaptation. In contrast, fast adaptive properties in the

fly visual system have been captured by a more computational, multiple pathway model that adapts as an intrinsic aspect of motion detection (Borst et al., 2005). Here, we find that all properties of retinal contrast adaptation are explained by a model with no such parallel pathway. Instead, transmission of the signal is naturally KU-57788 mouse coupled to an intrinsic adaptation not of the response, such that the process of transmitting a signal changes the rate of that transmission and depletes a store of that signal, leading to a change in temporal filtering, gain, and offset. Like adaptation to the mean luminance in the photoreceptor transduction cascade, contrast adaptation corresponds to a model of intrinsic adaptation. Other models of contrast adaptation have produced adaptive changes in sensitivity via a feedback pathway that subtracts a filtered version of the output signal

(Gaudry and Reinagel, 2007 and Victor, 1987). The LNK model differs in that the reduction of gain is produced not by a feedback inhibitory pathway, but rather by depleting a signal as it is transmitted. This architecture avoids the need for a feedback inhibitory pathway. Integrate-and-fire (IF) type models qualitatively cause adaptive gain changes and small changes in temporal filtering (Gaudry and Reinagel, 2007, Keat et al., 2001, Pillow et al., 2005 and Rudd and Brown, 1997). By comparison, the LNK model captures both neural responses and all adaptive properties across multiple contrasts, in particular full changes in kinetics and homeostatic fast and slow changes in response amplitude. For models of the IF type, each spike subtracts an afterpotential, causing refractoriness.