@article {4141, title = {Evidence that recurrent circuits are critical to the ventral stream{\textquoteright}s execution of core object recognition behavior}, journal = {Nature Neuroscience}, year = {2019}, month = {04/2019}, abstract = {
Non-recurrent deep convolutional neural networks (DCNNs) are currently the best models of core object recognition; a behavior supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. Are these recurrent circuits critical to the ventral stream{\textquoteright}s execution of this behavior? We reasoned that, if recurrence is critical, then primates should outperform feedforward-only DCNNs for some images, and that these images should require additional processing time beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these {\textquotedblleft}challenge{\textquotedblright} images. Second, using large- scale IT electrophysiology in animals performing core recognition tasks, we observed that behaviorally-sufficient, linearly-decodable object identity solutions emerged ~30ms (on average) later in IT for challenge images compared to DCNN and primate performance-matched {\textquotedblleft}control{\textquotedblright} images. We observed these same late solutions even during passive viewing. Third, consistent with a failure of feedforward computations, the behaviorally-critical late-phase IT population response patterns evoked by the challenge images were poorly predicted by DCNN activations. Interestingly, very deep CNNs as well as not-so-deep but recurrent CNNs better predicted these late IT responses, suggesting a functional equivalence between additional nonlinear transformations and recurrence. Our results argue that automatically-evoked recurrent circuits are critical even for rapid object identification. By precisely comparing current DCNNs, primate behavior and IT population dynamics, we provide guidance for future recurrent model development.
}, doi = {10.1038/s41593-019-0392-5}, url = {https://www.nature.com/articles/s41593-019-0392-5}, author = {Kohitij Kar and Jonas Kubilius and Kailyn Schmidt and Elias B. Issa and James J. DiCarlo} }