R is the reversal potential of the respective conductance. Vr (−50mV) is the cell’s resting potential. The firing rate was computed as [ΔV(θ) − Vthres]n+, where Vthres, the spike threshold, was 4mV (relative to rest) and exponent n was 3 ( Priebe et al., 2004). The subscript “+” indicates rectification, i.e., that values below zero were set to zero. The tuning properties of excitatory

and inhibitory synaptic conductances (i.e., σ, gmin, gmax) in layer 2/3 Pyr cells were determined using whole-cell recordings voltage-clamp configuration where cells were held at the reversal potential for inhibition and excitation, respectively. The average visually evoked conductance was then determined for each of the six orientation of drifting gratings presented Raf inhibitor ( Figure 5C). The result was fit with a Gaussian, gX. Statistical significance was determined using the Wilcoxon sign rank, and rank sum tests where appropriate. We would like to thank C. Niell FK228 solubility dmso and M. Stryker for providing expertise and sharing code at the initial stages of this project; H. Adesnik for his help implementing optogenetic approaches; S.R. Olsen for his insights and help in developing the visual recording configuration; and J. Evora, A.N. Linder, and P. Abelkop

for histology and mouse husbandry. M.C. holds the GlaxoSmithKline / Fight for Sight Chair in Visual Neuroscience. B.V.A. was supported by NIH NS061521. This work was supported by the Gatsby Charitable Foundation and HHMI. “
“We perceive a world filled with three-dimensional (3D) objects even though 3D objects are projected onto a two-dimensional (2D) retinal image. Hence, the perception of 3D structures needs to be constructed by the brain. Yet, how and where 3D-structure perception arises from the activity of neurons within the brain remains an unanswered question. One candidate for an area that could subserve 3D-structure perception is the inferotemporal (IT) cortex. IT contains shape-selective neurons whose responses are typically tolerant to various image

transformations such as changes in size, position (in depth), or defining below cue (Ito et al., 1995, Janssen et al., 2000, Sáry et al., 1993, Schwartz et al., 1983 and Vogels, 1999). These properties make it likely that IT neurons underlie object recognition and categorization (Logothetis and Sheinberg, 1996 and Tanaka, 1996). Nonetheless, it has thus far proved difficult to unequivocally relate IT neurons having particular shape preferences to a given perceptual behavior that relies on the information encoded by those neurons. Moreover, although the representation of 3D structure is intrinsically linked to the representation of objects, the third shape dimension has hitherto received relatively little attention. The 3D structure of objects can be signaled by a variety of depth cues (Howard and Rogers, 1995).