Figure 4. IT single unit properties and their relationship to population performance.

(A) Post stimulus spike histogram from an example IT neuron to one object image (a chair) that was the most effective among 213 tested object images (Zoccolan et al., 2007). (B) Left: The mean responses of the same IT neuron to each of 213 object images (based on spike rate in the gray time window in A). Object images are ranked according to their effectiveness in driving the neuron. As is typical, the neuron responded strongly to ~10% of objects images (four example images of nearly equal effectiveness are shown) and was suppressed below background rate by other objects (two example images shown), with no obvious indication of what critical features triggered or suppressed its firing. Colors indicate highly-effective (red), medium-effective (blue) and poorly-effective (green) images. Right: Data from a second study (new IT neuron) using natural images patches to illustrate the same point (Rust and DiCarlo, unpublished). (C) Response profiles from an example IT neuron obtained by varying the position (elevation) of three objects with high (red), medium (blue), and (low) effectiveness. While response magnitude is not preserved, the rank-order object identity preference is maintained along the entire tested range of tested positions. (D) To explain data in C, each IT neuron (right panel) is conceptualized as having joint, separable tuning for shape (identity) variables and for identity-preserving variables (e.g. position). If a population of such IT neurons tiles that space of variables (left panel), the resulting population representation conveys untangled object identity manifolds (Fig. 2B, right), while still conveying information about other variables such as position, size, etc. (Li et al., 2009). (E) Direct tests of untangled object identity manifolds consist of using simple decoders (e.g. linear classifiers) to measure the cross-validated population performance on categorization tasks (adapted from (Hung et al., 2005; Rust and DiCarlo, 2010). Performance magnitude approaches ceiling level with only a few hundred neurons (left panel), and the same population decode gives nearly perfect generalization across moderate changes in position (1.5 deg and 3 deg shifts), scale (0.5x/2x and 0.33x/3x), and context (right panel), which is consistent with previous work (Hung et al., 2005); right bar) and with the simulations in (D).