Moreover, the space of alternative algorithms is vague because industrial algorithms are not typically published, “new” object recognition algorithms from the academic community appear every few months, and there is little incentive to produce algorithms this website as downloadable, well-documented code. Visual psychophysicists have traditionally worked in highly restricted stimulus domains and with tasks that are thought to provide cleaner inference about the internal workings of the visual system. There is little incentive to systematically

benchmark real-world object recognition performance for consumption by computational or experimental laboratories. Fortunately, we are seeing increasing calls for meaningful collaboration by funding agencies, and collaborative groups are now working on all three pieces of the problem: (1) collecting the relevant Kinase Inhibitor Library cell assay psychophysical data, (2) collecting the relevant neuroscience data, and (3) putting together large numbers of alternative, instantiated computational models (algorithms) that work on real images (e.g., Cadieu et al., 2007, Zoccolan et al.,

2007 and Pinto et al., 2009b, 2010; Majaj et al., 2012). We do not yet fully know how the brain solves object recognition. The first step is to clearly define the question itself. “Core object recognition,” the ability to rapidly recognize objects in the central visual field in the face of image variation, is a problem that, if solved, will be the cornerstone for understanding biological DNA ligase object recognition. Although systematic characterizations of behavior are still ongoing, the brain has already revealed its likely

solution to this problem in the spiking patterns of IT populations. Human-like levels of performance do not appear to require extensive recurrent communication, attention, task dependency, or complex coding schemes that incorporate precise spike timing or synchrony. Instead, experimental and theoretical results remain consistent with this parsimonious hypothesis: a largely feedforward, reflexively computed, cascaded scheme in which visual information is gradually transformed and retransmitted via a firing rate code along the ventral visual pathway, and presented for easy downstream consumption (i.e., simple weighted sums read out from the distributed population response). To understand how the brain computes this solution, we must consider the problem at different levels of abstraction and the links between those levels. At the neuronal population level, the population activity patterns in early sensory structures that correspond to different objects are tangled together, but they are gradually untangled as information is re-represented along the ventral stream and in IT.