Significance for all analyses was determined by p < 0.05. We thank Dr. Rueben A. Gonzales at the University of Texas for the generous use of his gas chromatograph for the analysis of brain ethanol samples. The authors are supported by grants from the National Institutes of Health, CP-868596 in vivo NIDA DA09411, NINDS NS21229, and the Cancer Prevention and Research Institute of Texas. The authors

also acknowledge the support arising from the joint participation of the Diana Helis Henry Medical Research Foundation through its direct engagement with Baylor College of Medicine and the “Genomic, Neural, Preclinical Analysis for Smoking Cessation” Project for the Cancer Program. W.M.D. was also supported by an NRSA (F32 AA016709). “
“Visual scenes are correlated in space and time due to the properties of environmental conditions, objects, eye movements, and self motion (Field, 1987 and Frazor and Geisler, 2006). Because of this statistical regularity, it has long been thought that the visual system might improve its efficiency and performance by adjusting its response properties to the recent history of visual

input (Barlow et al., 1957, Blakemore and Campbell, 1969 and Laughlin, 1981). In early sensory systems, studies of how stimulus statistics influence the neural code have focused mainly on adaptation. Given AZD8055 mouse the recent stimulus distribution, response properties change over multiple timescales to encode more information and remove predictable parts of the stimulus (Fairhall et al., 2001, Hosoya et al., 2005, Ozuysal and Baccus, 2012 and Wark et al., 2009). Underlying studies of adaptation is the idea that early sensory systems should maximize information transmission for processing in the higher brain (Atick, 1992 and van Hateren, 1997). Studies in the higher brain and behavior often have a different perspective: the goal is to generate a behavior given a stimulus (Körding and Wolpert, 2006, Schwartz et al., 2007 and Yuille and Kersten, 2006). Accordingly, such studies have revealed that choosing the appropriate action benefits from predicting future stimuli by performing Casein kinase 1 an

ongoing inference based on the prior probability of sensory input. Recent results indicate that many ganglion cells encode specific features with a sharp threshold, implying that these ganglion cells make a decision as to the presence of a feature (Olveczky et al., 2003 and Zhang et al., 2012). If so, one might expect that retinal plasticity also take advantage of the principles of signal detection and optimal inference. At the photoreceptor-to-bipolar-cell synapse, even though at the dimmest light level the synapse threshold is close to the optimal level for signal detection, it does not appear that any adjustment occurs due to the prior signal probability (Field and Rieke, 2002). This problem, however, has not been explored in ganglion cells. Given the complex circuitry of the inner retina and the different types of ganglion cell plasticity (Hosoya et al.