How do these IT neuronal population phenomena (above) depend on the responses of individual IT neurons? Understanding IT single-unit responses has proven to be extremely challenging and while some progress has been made (Brincat and Connor, 2004 and Yamane et al., 2008), we still have a poor ability to build encoding models

that predict the responses of each IT neuron to new images (see Figure 4B). Nevertheless, we know that IT neurons are activated by at least moderately complex combinations of visual features (Brincat and Connor, 2004, Desimone et al., 1984, Kobatake and Tanaka, 1994b, Perrett et al., 1982, Rust and DiCarlo, FK228 order 2010 and Tanaka, 1996) and that they are often able to maintain their relative object preference over small to moderate changes in object position and size (Brincat and Connor, 2004, Ito et al., 1995, Li et al., 2009, Rust and DiCarlo, 2010 and Tovée et al., 1994), pose (Logothetis et al., 1994), illumination (Vogels and Biederman, 2002), and clutter (Li et al., 2009, Missal et al., 1999, Missal et al., 1997 and Zoccolan et al., 2005). Contrary to popular depictions of IT neurons as narrowly selective “object detectors,” neurophysiological studies of IT are in selleck chemicals near universal agreement with early accounts that describe a diversity of selectivity: “We found that, as in other visual areas, most IT neurons respond to many different

visual stimuli and, thus, cannot be narrowly tuned ‘detectors’ for particular complex objects…” (Desimone et al., 1984).

For example, studies that involve probing the responses of IT cells with large and diverse stimulus sets show that, while some neurons appear highly selective for particular objects, they are the exception not the rule. Instead, most IT neurons are broadly these tuned and the typical IT neuron responds to many different images and objects (Brincat and Connor, 2004, Freedman et al., 2006, Kreiman et al., 2006, Logothetis et al., 1995, Op de Beeck et al., 2001, Rolls, 2000, Rolls and Tovee, 1995, Vogels, 1999 and Zoccolan et al., 2007; see Figure 4B). In fact, the IT population is diverse in both shape selectivity and tolerance to identity-preserving image transformations such as changes in object size, contrast, in-depth and in-plane rotation, and presence of background or clutter (Ito et al., 1995, Logothetis et al., 1995, Op de Beeck and Vogels, 2000, Perrett et al., 1982, Rust and DiCarlo, 2010, Zoccolan et al., 2005 and Zoccolan et al., 2007). For example, the standard deviation of IT receptive field sizes is approximately 50% of the mean (mean ± SD: 16.5° ± 6.1°, Kobatake and Tanaka, 1994b; 24.5° ± 15.7°, Ito et al., 1995; and 10° ± 5°, Op de Beeck and Vogels, 2000). Moreover, IT neurons with the highest shape selectivities are the least tolerant to changes in position, scale, contrast, and presence of visual clutter ( Zoccolan et al.