@article {181, title = {The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates}, journal = {arXiv}, year = {2023}, month = {12/10/2023}, type = {preprint}, abstract = {

Visual object recognition -- the behavioral ability to rapidly and accurately categorize many visually encountered objects -- is core to primate cognition. This behavioral capability is algorithmically impressive because of the myriad identity-preserving viewpoints and scenes that dramatically change the visual image produced by the same object. Until recently, the brain mechanisms that support that capability were deeply mysterious. However, over the last decade, this scientific mystery has been illuminated by the discovery and development of brain-inspired, image-computable, artificial neural network (ANN) systems that rival primates in this behavioral feat. Apart from fundamentally changing the landscape of artificial intelligence (AI), modified versions of these ANN systems are the current leading scientific hypotheses of an integrated set of mechanisms in the primate ventral visual stream that support object recognition. What separates brain-mapped versions of these systems from prior conceptual models is that they are Sensory-computable, Mechanistic, Anatomically Referenced, and Testable (SMART). Here, we review and provide perspective on the brain mechanisms that the currently leading SMART models address. We review the empirical brain and behavioral alignment successes and failures of those current models. Given ongoing advances in neurobehavioral measurements and AI, we discuss the next frontiers for even more accurate mechanistic understanding. And we outline the likely applications of that SMART-model-based understanding.

}, doi = { https://doi.org/10.48550/arXiv.2312.05956}, url = {https://arxiv.org/pdf/2312.05956.pdf}, author = {Kohitij Kar and James J DiCarlo} } @article {6, title = {Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition}, journal = {BioRxiv}, year = {2020}, month = {05/2020}, type = {preprint}, abstract = {

Distributed neural population spiking patterns in macaque inferior temporal (IT) cortex that support core visual object recognition require additional time to develop for specific (\"late-solved\") images suggesting the necessity of recurrent processing in these computations. Which brain circuit motifs are most responsible for computing and transmitting these putative recurrent signals to IT? To test whether the ventral prefrontal cortex (vPFC) is a critical recurrent circuit node in this system, here we pharmacologically inactivated parts of the vPFC and simultaneously measured IT population activity, while monkeys performed object discrimination tasks. Our results show that vPFC inactivation deteriorated the quality of the late-phase (\>150 ms from image onset) IT population code, along with commensurate, specific behavioral deficits for \"late-solved\" images. Finally, silencing vPFC caused the monkeys\&$\#$39; IT activity patterns and behavior to become more like those produced by feedforward artificial neural network models of the ventral stream. Together with prior work, these results argue that fast recurrent processing through the vPFC is critical to the production of behaviorally-sufficient object representations in IT.

}, doi = {https://doi.org/10.1101/2020.05.10.086959}, url = {https://www.biorxiv.org/content/10.1101/2020.05.10.086959v1}, author = {Kohitij Kar and James J. DiCarlo} } @conference {11, title = {Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs}, booktitle = {Neural Information Processing Systems}, year = {2019}, abstract = {

Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep and branching architectures, demonstrating increasingly better object categorization performance, yet bringing into question how brain-like they still are. In particular, typical deep models from the machine learning community are often hard to map onto the brain\&$\#$39;s anatomy due to their vast number of layers and missing biologically-important connections, such as recurrence. Here we demonstrate that better anatomical alignment to the brain and high performance on machine learning as well as neuroscience measures do not have to be in contradiction. We developed CORnet-S, a shallow ANN with four anatomically mapped areas and recurrent connectivity, guided by Brain-Score, a new large-scale composite of neural and behavioral benchmarks for quantifying the functional fidelity of models of the primate ventral visual stream. Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet. Moreover, our extensive analyses of CORnet-S circuitry variants reveal that recurrence is the main predictive factor of both Brain-Score and ImageNet top-1 performance. Finally, we report that the temporal evolution of the CORnet-S \"IT\" neural population resembles the actual monkey IT population dynamics. Taken together, these results establish CORnet-S, a compact, recurrent ANN, as the current best model of the primate ventral visual stream.

}, doi = {https://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns}, url = {https://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns.pdf}, author = {Jonas Kubilius and Martin Schrimpf and Ha Hong and Najib Majaj and Rajalingham, Rishi and Issa, Elias B. and Kohitij Kar and Bashivan, Pouya and Jonathan Prescott-Roy and Kailyn Schmidt and Aran Nayebi and Daniel Bear and Daniel L. K. Yamins and James J. DiCarlo} }