The remarkable similarity of these properties across species and sensory systems indicates

a strong commonality in the encoding of signals that vary in amplitude (Baccus, 2006, Baccus and Meister, 2002 and Fairhall et al., 2001 ; Nagel and Doupe, 2006). In the vertebrate retina, although all of these adaptive changes are observed among ganglion cells and some amacrine cells, there is diversity in the selleck compound adaptive properties of different cell populations. For example, Off cells change their gain more than On cells, and On cells show less of a change in temporal processing (Beaudoin et al., 2008 and Chander and Chichilnisky, 2001). Bipolar cells also vary in their adaptive properties, with some cells not adapting, whereas others change only their gain or their temporal processing, or do not exhibit slow changes in baseline (Baccus and Meister, 2002 and Rieke, 2001). There is also diversity in the potential mechanisms that have been proposed for contrast

adaptation in retinal ganglion cells (Demb, 2008). Inactivation of voltage-dependent Na channels in ganglion cells can quickly change the gain (Kim and Rieke, 2003). In addition, a large fraction of adaptation Trametinib solubility dmso occurs as the signal travels through the synapse from bipolar to ganglion cell (Beaudoin et al., 2007 and Zaghloul et al., 2005). A change in basal vesicle release is proposed to cause slow contrast adaptation, and another calcium-related mechanism, such as channel inactivation, might cause fast adaptation (Beaudoin et al., 2008, Demb, 2008 and Manookin and Demb, 2006). Across sensory systems, a substantial difficulty in connecting the apparently complex and diverse phenomena of variance adaptation with the set of potential cellular mechanisms is the lack

of a quantitative model that captures both the immediate many sensory response and all adaptive properties. Although several models have been proposed for contrast adaptation (Gaudry and Reinagel, 2007 ; Mante et al., 2008 ; Shapley and Victor, 1979), they focused on only a few aspects of adaptation or used abstract components that lack a clear connection to biophysical mechanisms. In addition, previous efforts to describe the rules of contrast adaptation using a model were constrained only by the firing rate of spiking neurons and not by the membrane potential response. Here, we present a simple theoretical framework that combines aspects of models previously used to capture sensory responses and cellular mechanisms, and use it to interpret the adaptive behavior of retinal neurons. Our goals were to accurately predict the intracellular membrane potential response to a uniform field stimulus with a constant mean intensity across a wide range of contrasts and to capture all adaptive properties with a model that has a natural relationship to biophysical properties. We also wanted the model to be sufficiently simple to allow insight into how its mechanics give rise to the multiple properties of adaptation.