@article {180, title = {Let{\textquoteright}s move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human visionAbstract}, journal = {Behavioral and Brain Sciences}, volume = {4634}, year = {2023}, month = {Jan-01-2023}, abstract = {

In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the brain and behavior, and they advocate for a fragmented, phenomenon-specific modeling approach. These are unconstructive to scientific progress. We outline how the Brain-Score community is moving forward to add new model-to-human comparisons to its community-transparent suite of benchmarks.

}, issn = {0140-525X}, doi = {10.1017/S0140525X23001607}, url = {https://www.cambridge.org/core/product/identifier/S0140525X23001607/type/journal_article}, author = {DiCarlo, James J. and Yamins, Daniel L. K. and Ferguson, Michael E. and Fedorenko, Evelina and Bethge, Matthias and Bonnen, Tyler and Schrimpf, Martin} } @article {177, title = {A Unifying Principle for the Functional Organization of Visual Cortex}, journal = {bioRxiv}, year = {2023}, month = {2023}, abstract = {

A key feature of many cortical systems is functional organization: the arrangement of neurons with specific functional properties in characteristic spatial patterns across the cortical surface. However, the principles\ underlying the emergence and utility of functional organization are poorly understood. Here we develop\ the Topographic Deep Artificial Neural Network (TDANN), the first unified model to accurately predict the\ functional organization of multiple cortical areas in the primate visual system. We analyze the key factorsresponsible for the TDANN\’s success and find that it strikes a balance between two specific objectives:achieving a task-general sensory representation that is self-supervised, and maximizing the smoothness of\ responses across the cortical sheet according to a metric that scales relative to cortical surface area. In\ turn, the representations learned by the TDANN are lower dimensional and more brain-like than those in\ models that lack a spatial smoothness constraint. Finally, we provide evidence that the TDANN\’s functionalorganization balances performance with inter-area connection length, and use the resulting models for\ a proof-of-principle optimization of cortical prosthetic design. Our results thus offer a unified principle\ for understanding functional organization and a novel view of the functional role of the visual system in\ particular.

}, doi = { https://doi.org/10.1101/2023.05.18.541361}, url = {https://www.biorxiv.org/content/10.1101/2023.05.18.541361v1.full.pdf}, author = {Margalit, Eshed and Lee, Hyodong and Finzi, Dawn and DiCarlo, James J and Grill-Spector, Kalanit and Yamins, Daniel LK} } @article {171, title = {Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness}, journal = {bioRxiv}, year = {2022}, month = {July 4, 20202}, type = {preprint}, abstract = {

While some state-of-the-art artificial neural network systems in computer vision are strikingly accurate models of the corresponding primate visual processing, there are still many discrepancies between these models and the behavior of primates on object recognition tasks. Many current models suffer from extreme sensitivity to adversarial attacks and often do not align well with the image-by-image behavioral error patterns observed in humans. Previous research has provided strong evidence that primate object recognition behavior can be very accurately predicted by neural population activity in the inferior temporal (IT) cortex, a brain area in the late stages of the visual processing hierarchy. Therefore, here we directly test whether making the late stage representations of models more similar to that of macaque IT produces new models that exhibit more robust, primate-like behavior. We conducted chronic, large-scale multi-electrode recordings across the IT cortex in six non-human primates (rhesus macaques). We then use these data to fine-tune (end-to-end) the model \“IT\” representations such that they are more aligned with the biological IT representations, while preserving accuracy on object recognition tasks. We generate a cohort of models with a range of IT similarity scores validated on held-out animals across two image sets with distinct statistics. Across a battery of optimization conditions, we observed a strong correlation between the models\’ IT-likeness and alignment with human behavior, as well as an increase in its adversarial robustness. We further assessed the limitations of this approach and find that the improvements in behavioral alignment and adversarial robustness generalize across different image statistics, but not to object categories outside of those covered in our IT training set. Taken together, our results demonstrate that building models that are more aligned with the primate brain leads to more robust and human-like behavior, and call for larger neural data-sets to further augment these gains.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {https://doi.org/10.1101/2022.07.01.498495}, url = {https://www.biorxiv.org/content/10.1101/2022.07.01.498495v1.full.pdf}, author = {Dapello, Joel and Kar, Kohitij and Schrimpf, Martin and Geary, Robert and Ferguson, Michael and Cox, David D. and DiCarlo, James J.} } @conference {169, title = {Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2021}, address = {Lisbon, Portugal}, abstract = {

Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems. Recent work has proposed adding biologically-inspired components to visual neural networks as a way to improve their adversarial robustness. One surprisingly effective component for reducing adversarial vulnerability is response stochasticity, like that exhibited by biological neurons. Here, using recently developed geometrical techniques from computational neuroscience, we investigate how adversarial perturbations influence the internal representations of standard, adversarially trained, and biologically-inspired stochastic networks. We find distinct geometric signatures for each type of network, revealing different mechanisms for achieving robust representations. Next, we generalize these results to the auditory domain, showing that neural stochasticity also makes auditory models more robust to adversarial perturbations. Geometric analysis of the stochastic networks reveals overlap between representations of clean and adversarially perturbed stimuli, and quantitatively demonstrate that competing geometric effects of stochasticity mediate a tradeoff between adversarial and clean performance. Our results shed light on the strategies of robust perception utilized by adversarially trained and stochastic networks, and help explain how stochasticity may be beneficial to machine and biological computation.

}, url = {https://proceedings.neurips.cc/paper/2021/file/8383f931b0cefcc631f070480ef340e1-Paper.pdf}, author = {Joel Dapello and Jenelle Feather and Tiago Marques and David Cox and Josh McDermott and James J DiCarlo and Sueyeon Chung} } @article {167, title = {Unsupervised neural network models of the ventral visual stream}, journal = {Proceedings of the National Academy of Sciences}, volume = {118}, year = {2021}, month = {Jul-01-2022}, pages = {e2014196118}, abstract = {

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today\’s best supervised methods and that the mapping of these neural network models\’ hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.

}, issn = {0027-8424}, doi = {10.1073/pnas.2014196118}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2014196118}, author = {Zhuang, Chengxu and Yan, Siming and Nayebi, Aran and Schrimpf, Martin and Frank, Michael C. and DiCarlo, James J. and Yamins, Daniel L. K.} } @article {152, title = {An Open Resource for Non-human Primate Optogenetics}, journal = {Neuron}, year = {2020}, month = {January 10, 2020}, type = {NeuroResource}, abstract = {

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

}, issn = {08966273}, doi = {10.1016/j.neuron.2020.09.027}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627320307510}, author = {Tremblay, Sebastien and Acker, Leah and Afraz, Arash and Albaugh, Daniel L. and Amita, Hidetoshi and Andrei, Ariana R. and Angelucci, Alessandra and Aschner, Amir and Balan, Puiu F. and Basso, Michele A. and Benvenuti, Giacomo and Bohlen, Martin O. and Caiola, Michael J. and Calcedo, Roberto and Cavanaugh, James and Chen, Yuzhi and Chen, Spencer and Chernov, Mykyta M. and Clark, Andrew M. and Dai, Ji and Debes, Samantha R. and Deisseroth, Karl and Desimone, Robert and Dragoi, Valentin and Egger, Seth W. and Eldridge, Mark A.G. and El-Nahal, Hala G. and Fabbrini, Francesco and Federer, Frederick and Fetsch, Christopher R. and Fortuna, Michal G. and Friedman, Robert M. and Fujii, Naotaka and Gail, Alexander and Galvan, Adriana and Ghosh, Supriya and Gieselmann, Marc Alwin and Gulli, Roberto A. and Hikosaka, Okihide and Hosseini, Eghbal A. and Hu, Xing and {\"u}er, Janina and Inoue, Ken-ichi and Janz, Roger and Jazayeri, Mehrdad and Jiang, Rundong and Ju, Niansheng and Kar, Kohitij and Klein, Carsten and Kohn, Adam and Komatsu, Misako and Maeda, Kazutaka and Martinez-Trujillo, Julio C. and Matsumoto, Masayuki and Maunsell, John H.R. and Mendoza-Halliday, Diego and Monosov, Ilya E. and Muers, Ross S. and Nurminen, Lauri and Ortiz-Rios, Michael and {\textquoteright}Shea, Daniel J. and Palfi, {\'e}phane and Petkov, Christopher I. and Pojoga, Sorin and Rajalingham, Rishi and Ramakrishnan, Charu and Remington, Evan D. and Revsine, Cambria and Roe, Anna W. and Sabes, Philip N. and Saunders, Richard C. and Scherberger, {\"o}rg and Schmid, Michael C. and Schultz, Wolfram and Seidemann, Eyal and Senova, Yann-Suhan and Shadlen, Michael N. and Sheinberg, David L. and Siu, Caitlin and Smith, Yoland and Solomon, Selina S. and Sommer, Marc A. and Spudich, John L. and Stauffer, William R. and Takada, Masahiko and Tang, Shiming and Thiele, Alexander and Treue, Stefan and Vanduffel, Wim and Vogels, Rufin and Whitmire, Matthew P. and Wichmann, Thomas and Wurtz, Robert H. and Xu, Haoran and Yazdan-Shahmorad, Azadeh and Shenoy, Krishna V. and DiCarlo, James J. and Platt, Michael L.} } @article {148, title = {ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation}, journal = {arXiv}, year = {2020}, month = {July 9, 2020}, type = {preprint}, abstract = {

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. With TDW, users can simulate high-fidelity sensory data and physical interactions between mobile agents and objects in a wide variety of rich 3D environments. TDW has several unique properties: 1) realtime near photo-realistic image rendering quality; 2) a library of objects and environments with materials for high-quality rendering, and routines enabling user customization of the asset library; 3) generative procedures for efficiently building classes of new environments 4) high-fidelity audio rendering; 5) believable and realistic physical interactions for a wide variety of material types, including cloths, liquid, and deformable objects; 6) a range of \"avatar\" types that serve as embodiments of AI agents, with the option for user avatar customization; and 7) support for human interactions with VR devices. TDW also provides a rich API enabling multiple agents to interact within a simulation and return a range of sensor and physics data representing the state of the world. We present initial experiments enabled by the platform around emerging research directions in computer vision, machine learning, and cognitive science, including multi-modal physical scene understanding, multi-agent interactions, models that \"learn like a child\", and attention studies in humans and neural networks. The simulation platform will be made publicly available.

}, url = {https://arxiv.org/abs/2007.04954}, author = {Gan, Chuang and Schwartz, Jeremy and Alter, Seth and Schrimpf, Martin and Traer, James and De Freitas, Julian and Kubilius, Jonas and Bhandwaldar, Abhishek and Haber, Nick and Sano, Megumi and Wang, Elias and Mrowca, Damian and Lingelbach, Michael and Curtis, Aidan and Figelis, Kevin and Bear, Daniel M. and Gutfreund, Dan and Cox, David and DiCarlo, James J. and McDermott, Josh and Tenenbaum, Joshua B. and Yamins, Daniel L.K.} } @article {150, title = {Unsupervised Neural Network Models of the Ventral Visual Stream}, journal = {bioRxiv}, year = {2020}, month = {June 18, 2020}, type = {preprint}, abstract = {

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today\’s best supervised methods, and that the mapping of these neural network models\’ hidden layers is neuroanatomically consistent across the ventral stream. Moreover, we find that these methods produce brain-like representations even when trained on noisy and limited data measured from real children\’s developmental experience. We also find that semi-supervised deep contrastive embeddings can leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results suggest that deep contrastive embedding objectives may be a biologically-plausible computational theory of primate visual development.

}, doi = {10.1101/2020.06.16.155556}, url = {https://www.biorxiv.org/content/10.1101/2020.06.16.155556v1.abstract}, author = {Zhuang, Chengxu and Yan, Siming and Nayebi, Aran and Schrimpf, Martin and Frank, Michael and DiCarlo, James J. and Yamins, Daniel L.K.} } @inbook {12, title = {Funcitional Properties of Circuits, Cellular Populations, and Areas}, booktitle = {The Neocortex}, volume = {27}, year = {2019}, pages = {223-265}, publisher = {The MIT Press}, organization = {The MIT Press}, chapter = {13}, address = {Cambridge, MA}, issn = {978-0-262-04324-3}, doi = {10.7551/mitpress/12593.001.0001}, url = {http://www.esforum.de}, author = {Harris, KD and Groh, JM and DiCarlo, JJ and Fries, P and Kaschube, M and Laurent, G and MacLean, JN and McCormick, DA and Pipa, G and Reynolds, JN and Schwartz, AB and Sejnowksi, TJ and Singer, W and Vinck, M}, editor = {Singer, Wolf and Sejnowski, Terrence J. and Rakic, Pasko} } @article {56, title = {A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation}, journal = {PLoS Computational Biology}, volume = {5}, year = {2009}, month = {2009}, pages = {e1000579}, abstract = {

While many models of biological object recognition share a common set of \"broad-stroke\" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model\–e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct \"parts\" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end {NVIDIA} graphic cards and the {PlayStation} 3\&$\#$39;s {IBM} Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.

}, doi = {10.1371/journal.pcbi.1000579}, url = {https://dx.plos.org/10.1371/journal.pcbi.1000579}, author = {Pinto, Nicolas and Doukhan, David and DiCarlo, James J. and Cox, David D.}, editor = {Friston, Karl J.} } @article {65, title = {Why is Real-World Visual Object Recognition Hard?}, journal = {PLoS Computational Biology}, volume = {4}, year = {2008}, month = {01/2008}, pages = {e27}, abstract = {

Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain\&$\#$39;s anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, \"natural\" images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled \"natural\" images in guiding that progress. In particular, we show that a simple V1-like model\–a neuroscientist\&$\#$39;s \"null\" model, which should perform poorly at real-world visual object recognition tasks\–outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a \"simpler\" recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition\–real-world image variation.

}, doi = {10.1371/journal.pcbi.0040027}, url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0040027}, author = {Pinto, Nicolas and Cox, David D and DiCarlo, James J}, editor = {Friston, Karl J} } @inbook {113, title = {Form processing and attention effects in somatosensory cortex}, booktitle = {Somesthesis and the Neurobiology of the Somatosensory Cortex}, year = {1996}, publisher = {Birkhauser Basel}, organization = {Birkhauser Basel}, address = {Switzerland}, issn = {978-3-0348-9016-8}, author = {Hsiao, Steven S. and Johnson, Kenneth O and Twombly, IA and DiCarlo, James J.}, editor = {Franzen, O and Johansson, R and Terenius, L} }