@article {160, title = {Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex}, journal = {eLife}, volume = {10}, year = {2021}, month = {Nov-06-2021}, abstract = {

Temporal continuity of object identity is a feature of natural visual input, and is potentially exploited -- in an unsupervised manner -- by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here we investigated whether plasticity of individual IT neurons underlies human core-object-recognition behavioral changes induced with unsupervised visual experience. We built a single-neuron plasticity model combined with a previously established IT population-to-recognition-behavior linking model to predict human learning effects. We found that our model, after constrained by neurophysiological data, largely predicted the mean direction, magnitude and time course of human performance changes. We also found a previously unreported dependency of the observed human performance change on the initial task difficulty. This result adds support to the hypothesis that tolerant core object recognition in human and non-human primates is instructed -- at least in part -- by naturally occurring unsupervised temporal contiguity experience.

}, doi = {10.7554/eLife.60830}, url = {https://elifesciences.org/articles/60830https://cdn.elifesciences.org/articles/60830/elife-60830-v2.pdfhttps://cdn.elifesciences.org/articles/60830/elife-60830-v2.xml}, author = {Jia, Xiaoxuan and Hong, Ha and DiCarlo, James J} } @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 {27, title = {Explicit information for category-orthogonal object properties increases along the ventral stream}, journal = {Nature Neuroscience}, volume = {19}, year = {2016}, month = {02/2016}, pages = {613 - 622}, abstract = {

Extensive research has revealed that the ventral visual stream hierarchically builds a robust representation for supporting visual object categorization tasks. We systematically explored the ability of multiple ventral visual areas to support a variety of \&$\#$39;category-orthogonal\&$\#$39; object properties such as position, size and pose. For complex naturalistic stimuli, we found that the inferior temporal (IT) population encodes all measured category-orthogonal object properties, including those properties often considered to be low-level features (for example, position), more explicitly than earlier ventral stream areas. We also found that the IT population better predicts human performance patterns across properties. A hierarchical neural network model based on simple computational principles generates these same cross-area patterns of information. Taken together, our empirical results support the hypothesis that all behaviorally relevant object properties are extracted in concert up the ventral visual hierarchy, and our computational model explains how that hierarchy might be built.

}, issn = {1097-6256}, doi = {10.1038/nn.4247}, url = {http://www.nature.com/articles/nn.4247}, author = {Hong, Ha and Yamins, Daniel L K and Majaj, Najib J and DiCarlo, James J} } @article {32, title = {Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition}, journal = {PLoS Computational Biology}, volume = {10}, year = {2014}, month = {12/2014}, pages = {e1003963}, abstract = {

The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of \"kernel analysis\" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

}, doi = {10.1371/journal.pcbi.1003963}, url = {https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003963\&type=printable}, author = {Cadieu, Charles F. and Hong, Ha and Yamins, Daniel L. K. and Pinto, Nicolas and Ardila, Diego and Solomon, Ethan A. and Majaj, Najib J. and DiCarlo, James J.}, editor = {Bethge, Matthias} } @conference {42, title = {A unified neuronal population code fully explains human object recognition.}, booktitle = {Computation and Systems Neuroscience (COSYNE)}, year = {2012}, month = {02/2012}, address = {Salt Lake City, Utah, USA}, abstract = {

Our goal is to understand the neuronal mechanisms that underlie human visual object recognition (OR). While previous work has argued for qualitative links between neuronal responses in the ventral visual stream and human shape judgements, no study has asked which, if any, neuronal responses are quantitatively sufficient to explain broad domain human OR performance. The shift from qualitative to quantitative hypotheses requires a framework to link neuronal responses to behavior (\“unified code\”). Here we ask: is there a common neuronal basis (e.g., in IT cortex) and a simple (e.g., linear) transformation that will predict all of human OR performance? We first defined OR operationally by obtaining human psychophysical measurements using images that explore shape similarity and identity preserving image variation, resulting in OR benchmarks that span a range of difficulty. Using the same visual images, we measured neuronal responses in V4 and IT in two monkeys. We implemented 14 unified codes based on those neuronal data and computed cross-validated neuronal discriminability indices (d\’s) to compare to the human d\’s. The dynamic range across those d\’s sets a high bar for when a putative code is sufficient to explain behavior: it is not sufficient for a code to perform well (high d\’) or to match one d\’. Instead, a sufficient unified code must also emergently predict the entire pattern of behavior over all tasks. Remarkably, we found a few unified IT-based codes that meet this high bar. Interestingly, many other IT codes and all V4 codes are insufficient. While humans outperform computer vision systems on many of our OR tasks, their abilities reliably depend on the images tested. These dependencies in human performance are fully explained by a simple, unified reading of monkey ventral stream neurons, a feat unmatched by any computer vision system we tested

}, doi = {http://www.cosyne.org/c/index.php?title=Cosyne_12}, url = {http://cosyne.org/cosyne12/Cosyne2012_program_book.pdf}, author = {Majaj, Najib J and Hong, Ha and Solomon, EA and DiCarlo, James J.} }