Visual object recognition (OR) is a central problem in systems neuroscience, human psychophysics, and computer vision. A recognition system must be robust to image variation produced by different “views” of each object-- the so-called “invariance problem.” My laboratory aims to understand and emulate the primate brain's solution to this problem.

We have previously shown that a part of the non-human primate ventral visual stream (inferior temporal cortex, IT) rapidly and automatically conveys neuronal population rate codes that qualitatively solve the invariance problem for vision. But are such codes quantitatively sufficient to explain behavioral OR performance? Our results show that these codes are a powerful object representation, in that low complexity decoding tools can be applied to them to perfectly predict human performance over a large range of OR tasks.

But how does the brain build this powerful representation? High-throughput computational methods can be used to explore a large family of biologically-constrained neural network architectures. Using this approach, we have recently discovered that functional optimization of this large family leads to specific algorithms that predict the response properties of IT dramatically better than all previous models. This suggests that these networks have captured key encoding mechanisms of human OR, and that today’s computer vision algorithms are very close to emulating the power of the primate OR system.