@conference {166, title = {Using Brain-Score to Evaluate and Build Neural Networks for Brain-Like Object Recognition}, booktitle = {Computational and Systems Neuroscience (COSYNE)}, year = {2019}, address = {Denver, CO}, author = {Schrimpf, Martin and Kubilius, Jonas and Hong, Ha and Majaj, Najib and Rajalingham, Rishi and Issa, Elias B and Kar, Kohitij and Ziemba, Corey M and Bashivan, Pouya and Prescott-Roy, Jonathan and Schmidt, Kailyn and Yamins, Daniel LK and DiCarlo, James J} } @article {19, title = {Evidence that recurrent circuits are critical to the ventral stream{\textquoteright}s execution of core object recognition behavior}, journal = {bioRxiv}, year = {2018}, month = {06/2018}, type = {preprint}, 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 ventral stream\&$\#$39;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 \"challenge\" 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 \"control\" 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, deeper CNNs better predicted these late IT responses, suggesting a functional equivalence between recurrence and additional nonlinear transformations. 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 = {https://doi.org/10.1101/354753}, url = {https://www.biorxiv.org/content/10.1101/354753v1.full.pdf}, author = {Kar, Kohitij and Kubilius, Jonas and Schmidt, Kailyn and Issa, Elias B and DiCarlo, James J.} } @article {21, title = {Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks}, journal = {bioRxiv}, year = {2018}, month = {02/2018}, type = {preprint}, abstract = {

Primates\—including humans\—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC\ models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC\ models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC\ models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks\—such as those obtained here\—could serve as direct guides for discovering such models.

}, doi = {https://doi.org/10.1101/240614}, url = {https://www.biorxiv.org/content/10.1101/240614v4.full.pdf}, author = {Rajalingham, Rishi and Issa, Elias B and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.} } @article {15, title = {Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals.}, journal = {eLife}, volume = {7}, year = {2018}, month = {11/2018}, abstract = {

Ventral visual stream neural responses are dynamic, even for static image presentations. However, dynamical neural models of visual cortex are lacking as most progress has been made modeling static, time-averaged responses. Here, we studied population neural dynamics during face detection across three cortical processing stages. Remarkably,~30 milliseconds after the initially evoked response, we found that neurons in intermediate level areas decreased their responses to typical configurations of their preferred face parts relative to their response for atypical configurations even while neurons in higher areas achieved and maintained a preference for typical configurations. These hierarchical neural dynamics were inconsistent with standard feedforward circuits. Rather, recurrent models computing prediction errors between stages captured the observed temporal signatures. This model of neural dynamics, which simply augments the standard feedforward model of online vision, suggests that neural responses to static images may encode top-down prediction errors in addition to bottom-up feature estimates.

}, keywords = {Animals, Brain Mapping, Face, Humans, Macaca mulatta, Models, Neurological, Neurons, Pattern Recognition, Photic Stimulation, Reaction Time, Visual, Visual Cortex, Visual Perception}, issn = {2050-084X}, doi = {10.7554/eLife.42870}, url = {https://elifesciences.org/articles/42870https://cdn.elifesciences.org/articles/42870/elife-42870-v2.pdf}, author = {Issa, Elias B and Cadieu, Charles F and DiCarlo, James J} }