@booklet {185, title = {How does the primate brain combine generative and discriminative computations in vision?}, year = {2024}, abstract = {

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of \"inference\" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz\&$\#$39;s sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it. In this conception, vision inverts a generative model through an interrogation of the evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.

}, keywords = {discriminative model, generativemodel, Primatevision, visual inference}, doi = { https://doi.org/10.48550/arXiv.2401.06005}, url = {https://arxiv.org/pdf/2401.06005.pdf}, author = {Benjamin Peters and James J. DiCarlo and Todd Gureckis and Ralf Haefner and Leyla Isik and Joshua Tenenbaum and Talia Konkle and Thomas Naselaris and Kimberly Stachenfeld and Zenna Tavares and Doris Tsao and Ilker Yildirim and Nikolaus Kriegeskorte} } @article {174, title = {Catalyzing next-generation Artificial Intelligence through NeuroAI}, journal = {Nature Communications}, volume = {14}, year = {2023}, month = {Jan-12-2023}, pages = {1597}, abstract = {

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities \– inherited from over 500 million years of evolution \– that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

}, doi = {10.1038/s41467-023-37180-x}, url = {https://www.nature.com/articles/s41467-023-37180-xhttps://www.nature.com/articles/s41467-023-37180-x.pdfhttps://www.nature.com/articles/s41467-023-37180-x.pdfhttps://www.nature.com/articles/s41467-023-37180-x}, author = {Zador, Anthony and Escola, Sean and Richards, Blake and {\"O}lveczky, Bence and Bengio, Yoshua and Boahen, Kwabena and Botvinick, Matthew and Chklovskii, Dmitri and Churchland, Anne and Clopath, Claudia and DiCarlo, James and Ganguli, Surya and Hawkins, Jeff and {\"o}rding, Konrad and Koulakov, Alexei and LeCun, Yann and Lillicrap, Timothy and Marblestone, Adam and Olshausen, Bruno and Pouget, Alexandre and Savin, Cristina and Sejnowski, Terrence and Simoncelli, Eero and Solla, Sara and Sussillo, David and Tolias, Andreas S. and Tsao, Doris} } @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} } @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 {7, title = {Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex}, journal = {BioRxiv}, year = {2020}, month = {01.2020}, type = {preprint}, 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 and IT-dependent core object recognition behavior. Here we investigated whether plasticity of individual IT neurons underlies human behavioral changes induced with unsupervised visual experience by building 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 quite accurately 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 = {https://doi.org/10.1101/2020.01.13.900837}, url = {https://www.biorxiv.org/content/10.1101/2020.01.13.900837v2.full.pdf}, author = {Xiaoxuan Jia and Ha Hong 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} } @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} } @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 {22, title = {Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?}, journal = {bioRxiv}, year = {2018}, month = {09/2018}, type = {preprint}, abstract = {

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain\&$\#$39;s mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at \>= 70\% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brain\&$\#$39;s network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.

}, doi = {https://doi.org/10.1101/407007}, url = {https://www.biorxiv.org/content/10.1101/407007v2.full.pdf}, author = {Martin Schrimpf and Kubilius, Jonas and Ha Hong and Najib Majaj and Rajalingham, Rishi and Issa, Elias B. and Kar, Kohitij and Bashivan, Pouya and Jonathan Prescott-Roy and Schmidt, Kailyn and Daniel L. K. Yamins 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 {28, title = {Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance}, journal = {Journal of Neuroscience}, volume = {35}, year = {2015}, month = {06/2017}, pages = {13402 - 13418}, abstract = {

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (\"face patches\") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of \∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates.

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.5181-14.2015}, url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.5181-14.2015}, author = {Majaj, N. J. and Hong, H. and Solomon, E. A. and DiCarlo, J. 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} } @article {31, title = {Performance-optimized hierarchical models predict neural responses in higher visual cortex}, journal = {Proceedings of the National Academy of Sciences}, year = {2014}, month = {10/2014}, pages = {8619 - 8624}, abstract = {

The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model\’s categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model\’s intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization\—applied in a biologically appropriate model class\—can be used to build quantitative predictive models of neural processing.

}, issn = {0027-8424}, doi = {10.1073/pnas.1403112111}, url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1403112111}, author = {Yamins, D. L. K. and Hong, H. and Cadieu, C. F. and Solomon, E. A. and Seibert, D. and DiCarlo, J. J.} } @conference {37, title = {Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream}, booktitle = {Advances in Neural Information Processing Systems}, year = {2013}, month = {12/2013}, address = {Lake Tahoe, Nevada, United States.}, abstract = {

Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representational Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. fMRI voxel responses, neural firing rates) or in models (features). Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream. The model, while novel in the optimization procedure, further develops a long-standing functional hypothesis that the ventral visual stream is a hierarchically arranged series of processing stages optimized for visual object recognition.

}, doi = {https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream}, url = {https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream.pdf}, author = {Daniel L. K. Yamins and Ha Hong and Cadieu, Charles F and DiCarlo, James J.} } @article {36, title = {The Neural Representation Benchmark and its Evaluation on Brain and Machine}, journal = {arXiv}, year = {2013}, month = {01/2013}, type = {preprint}, abstract = {

A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible to directly test representational learning algorithms directly against the representations contained in neural systems. Here, we propose a new benchmark for visual representations on which we have directly tested the neural representation in multiple visual cortical areas in macaque (utilizing data from [Majaj et al., 2012]), and on which any computer vision algorithm that produces a feature space can be tested. The benchmark measures the effectiveness of the neural or machine representation by computing the classification loss on the ordered eigendecomposition of a kernel matrix [Montavon et al., 2011]. In our analysis we find that the neural representation in visual area IT is superior to visual area V4. In our analysis of representational learning algorithms, we find that three-layer models approach the representational performance of V4 and the algorithm in [Le et al., 2012] surpasses the performance of V4. Impressively, we find that a recent supervised algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of IT for an intermediate level of image variation difficulty, and surpasses IT at a higher difficulty level. We believe this result represents a major milestone: it is the first learning algorithm we have found that exceeds our current estimate of IT representation performance. We hope that this benchmark will assist the community in matching the representational performance of visual cortex and will serve as an initial rallying point for further correspondence between representations derived in brains and machines.

}, doi = {https://arxiv.org/abs/1301.3530}, url = {https://arxiv.org/pdf/1301.3530.pdf}, author = {Cadieu, Charles F. and Ha Hong and Daniel L. K. Yamins and Pinto, Nicolas and Majaj, Najib J and James J. DiCarlo} } @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.} } @article {80, title = {Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex}, journal = {Neuron}, volume = {49}, year = {2006}, month = {01/2006}, pages = {433 - 445}, abstract = {

Local field potentials (LFPs) arise largely from dendritic activity over large brain regions and thus provide a measure of the input to and local processing within an area. We characterized (LFPs) and their relationship to spikes (multi and single unit) in monkey inferior temporal cortex (IT). LFP responses in IT to complex objects showed strong selectivity at 44\% of the sites and tolerance to retinal position and size. The LFP preferences were poorly predicted by the spike preferences at the same site but were better explained by averaging spikes within approximately 3 mm. A comparison of separate sites suggests that selectivity is similar on a scale of approximately 800 microm for spikes and approximately 5 mm for LFPs. These observations imply that inputs to IT neurons convey selectivity for complex shapes and that such input may have an underlying organization spanning several millimeters.

}, issn = {08966273}, doi = {10.1016/j.neuron.2005.12.019}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627305011311}, author = {Kreiman, Gabriel and Hung, Chou P. and Kraskov, Alexander and Quiroga, Rodrigo Quian and Poggio, Tomaso and DiCarlo, James J.} } @article {88, title = {Fast Readout of Object Identity from Macaque Inferior Temporal Cortex}, journal = {Science}, volume = {310}, year = {2005}, month = {04/2005}, pages = {863 - 866}, abstract = {

Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (approximately 100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object \"identity\" and \"category.\" This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.

}, keywords = {Action Potentials, Animals, Brain Mapping, Macaca mulatta, Neurons, Psychology, Psychomotor Performance, Recognition, Temporal Lobe, Time Factors, Visual Perception}, issn = {0036-8075}, doi = {10.1126/science.1117593}, url = {https://www.sciencemag.org/lookup/doi/10.1126/science.1117593}, author = {Hung, Chou P. and Kreiman, Gabriel and Poggio, Tomaso and DiCarlo, James J.} } @article {93, title = {Ultra-fast object recognition from few spikes}, number = {2005-022}, year = {2005}, month = {07/2005}, pages = {1-31}, institution = {MIT}, address = {Cambridge, MA}, abstract = {

Understanding the complex brain computations leading to object recognition requires quantitatively characterizing the information represented in inferior temporal cortex (IT), the highest stage of the primate visual stream. A read-out technique based on a trainable classifier is used to characterize the neural coding of selectivity and invariance at the population level. The activity of very small populations of independently recorded IT neurons (~100 randomly selected cells) over very short time intervals (as small as 12.5 ms) contains surprisingly accurate and robust information about both object \Â\‘identity\Â\’ and \Â\‘category\Â\’, which is furthermore highly invariant to object position and scale. Significantly, selectivity and invariance are present even for novel objects, indicating that these properties arise from the intrinsic circuitry and do not require object-specific learning. Within the limits of the technique, there is no detectable difference in the latency or temporal resolution of the IT information supporting so-called \Â\‘categorization\Â\’ (a.k. basic level) and \Â\‘identification\Â\’ (a.k. subordinate level) tasks. Furthermore, where information, in particular information about stimulus location and scale, can also be read-out from the same small population of IT neurons. These results show how it is possible to decode invariant object information rapidly, accurately and robustly from a small population in IT and provide insights into the nature of the neural code for different kinds of object-related information.

}, keywords = {AI, inferior temporal cortex, neural coding, object recognition}, issn = {2005-022}, url = {https://dspace.mit.edu/handle/1721.1/30556}, author = {Hung, Chou P. and Kreiman, Gabriel and Poggio, Tomaso and DiCarlo, James J.} } @proceedings {94, title = {Using {\textquoteleft}read-out{\textquoteright} of object identity to understand object coding in the macaque anterior inferior temporal cortex}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2005}, month = {03/2005}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, abstract = {

Recent efforts to develop robust computer vision systems capable of approaching the level of human object recognition performance have shown that models based on neurobiology can outperform non-biological models. To continue the development of such systems, we must understand the codes used by neuronal ensembles to represent object identity--a matter of continued debate in the neuroscience community. Understanding such codes would also allow the development of brain-machine interfaces capable of \‘reading-out\’ or \‘writing-in\’ information to AIT. One approach to understanding these neuronal codes is to obtain a large sample of neuronal data using a fixed set of objects, and then test the ability of various neuronal population measures (codes) to convey knowledge of object identity \– that is, determine the ability of each putative neuronal code to \‘read-out\’ object identity information from AIT. To this end, we have now recorded single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP), from over 500 sites in the anterior inferior temporal cortex (AIT) of two macaque monkeys while they viewed a fixed set of 77 complex objects. In this paper, we focus on the temporal latency and resolution of AIT neuronal population codes that best convey knowledge of object identity as assessed by object classification. Specifically, the 77 objects were divided before the experiment into eight different classes: toys, foodstuffs, human faces, monkey faces, hands, vehicles, boxes and cats/dogs. To evaluate the performance of each putative neuronal population code, we used support vector machines (SVM) and jackknife cross-validation to avoid over-fitting. Under the assumption of no covariance across spatially separate sites, these data and methods allowed us to examine putative neuronal population codes that differed in their latency (0 to 100 ms from stimulus onset) and their temporal resolution (12.5 to 200 ms wide bins). Results were evaluated for consistency across different sets of recording sites. We found that MUA and SUA spike count codes could perform single trial object classification. Performance improved with the number of sites and reached ~90\% when ~128 arbitrary AIT sites were included. For any given number of sites, increasing the temporal resolution beyond ~50 ms did not lead to improved performance and the optimal latency from stimulus onset was ~100 ms. Because the neuronal data were not collected simultaneously, we cannot rule out spatio-temporal codes with a time scale less than ~50 ms. However, these results support the hypothesis that the time scale of the object identity code in AIT is ~50 ms and that downstream read-out of object identity for behavior or further processing may simply integrate over this time scale.\ 

}, author = {Hung, Chou P. and Kreiman, Gabriel and Quiroga, Rodrigo Quian and Kraskov, Alexander and Poggio, Tomaso and DiCarlo, James J.} } @proceedings {96, title = {Object recognition by selective spike and LFP data in macaque inferior temporal cortex}, journal = {Society for Neuroscience}, year = {2004}, month = {10/2004}, publisher = {SFN}, address = {San Diego, CA, USA}, author = {Kreiman, Gabriel and Hung, Chou P. and Poggio, Tomaso and DiCarlo, James J.} } @article {98, title = {Selectivity of local field potentials in macaque inferior temporal cortex}, number = {2004-20}, year = {2004}, month = {09/2004}, institution = {MIT}, address = {Cambridge, M}, abstract = {

While single neurons in inferior temporal (IT) cortex show differential responses to distinct complex stimuli, little is known about the responses of populations of neurons in IT. We recorded single electrode data, including multi-unit activity (MUA) and local field potentials (LFP), from 618 sites in the inferior temporal cortex of macaque monkeys while the animals passively viewed 78 different pictures of complex stimuli. The LFPs were obtained by low-pass filtering the extracellular electrophysiological signal with a corner frequency of 300 Hz. As reported previously, we observed that spike counts from MUA showed selectivity for some of the pictures. Strikingly, the LFP data, which is thought to constitute an average over large numbers of neurons, also showed significantly selective responses. The LFP responses were less selective than the MUA responses both in terms of the proportion of selective sites as well as in the selectivity of each site. We observed that there was only little overlap between the selectivity of MUA and LFP recordings from the same electrode. To assess the spatial organization of selective responses, we compared the selectivity of nearby sites recorded along the same penetration and sites recorded from different penetrations. We observed that MUA selectivity was correlated on spatial scales up to 800 \&$\#$61549;m while the LFP selectivity was correlated over a larger spatial extent, with significant correlations between sites separated by several mm. Our data support the idea that there is some topographical arrangement to the organization of selectivity in inferior temporal cortex and that this organization may be relevant for the representation of object identity in IT.

}, keywords = {AI, inferior temporal cortex, local field potentials, object recognition}, issn = {2004-020}, url = {https://dspace.mit.edu/handle/1721.1/30417}, author = {Kreiman, Gabriel and Hung, Chou P. and Poggio, Tomasso and DiCarlo, James J.} } @article {110, title = {Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey}, journal = {The Journal of Neuroscience}, volume = {18}, year = {1998}, month = {04/1998}, pages = {2626 - 2645}, abstract = {

We investigated the two-dimensional structure of area 3b neuronal receptive fields (RFs) in three alert monkeys. Three hundred thirty neurons with RFs on the distal fingerpads were studied with scanned, random dot stimuli. Each neuron was stimulated continuously for 14 min, yielding 20,000 response data points. Excitatory and inhibitory components of each RF were determined with a modified linear regression algorithm. Analyses assessing goodness-of-fit, repeatability, and generality of the RFs were developed. Two hundred forty-seven neurons yielded highly repeatable RF estimates, and most RFs accounted for a large fraction of the explainable response of each neuron. Although the area 3b RF structures appeared to be continuously distributed, certain structural generalities were apparent. Most RFs (94\%) contained a single, central region of excitation and one or more regions of inhibition located on one, two, three, or all four sides of the excitatory center. The shape, area, and strength of excitatory and inhibitory RF regions ranged widely. Half the RFs contained almost evenly balanced excitation and inhibition. The findings indicate that area 3b neurons act as local spatiotemporal filters that are maximally excited by the presence of particular stimulus features. We believe that form and texture perception are based on high-level representations and that area 3b is an intermediate stage in the processes leading to these representations. Two possibilities are considered: (1) that these high-level representations are basically somatotopic and that area 3b neurons amplify some features and suppress others, or (2) that these representations are highly transformed and that area 3b effects a step in the transformation.

}, keywords = {Afferent, Animals, Data Interpretation, Electrophysiology, Female, Macaca mulatta, Male, Neural Inhibition, Neurons, Reproducibility of Results, Somatosensory Cortex, Statistical, Touch}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.18-07-02626.1998}, url = {http://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.18-07-02626.1998}, author = {DiCarlo, James J. and Johnson, Kenneth O. and Hsiao, Steven S.} } @proceedings {112, title = {Spatial and temporal properties of neural receptive fields in area 3b of the awake monkey}, journal = {Society for Neuroscience}, year = {1997}, month = {10/2007}, publisher = {SFN}, address = {New Orleans, LA, USA}, author = {DiCarlo, James J. and Hsiao, Steven S. and Johnson, Kenneth O} } @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} } @proceedings {114, title = {Laminar differences in spatiotemporal receptive field structure of neurons in area 3b of the awake macaque}, journal = {Society for Neuroscience}, year = {1996}, month = {10/1996}, publisher = {SFN}, address = {Washington, DC, USA}, author = {DiCarlo, James J. and Twombly, IA and Hsiao, Steven S. and Johnson, Kenneth O} } @proceedings {115, title = {Linear and non-linear processing of tactile spatial form in area 3b of the awake macaque}, journal = {Society for Neuroscience}, year = {1996}, month = {10/1996}, publisher = {SFN}, address = {Washington, DC, USA}, author = {Twombly, IA and DiCarlo, James J. and Hsiao, Steven S. and Johnson, Kenneth O} } @article {116, title = {Marking microelectrode penetrations with fluorescent dyes}, journal = {Journal of Neuroscience Methods}, volume = {64}, year = {1996}, month = {01/1996}, pages = {75 - 81}, abstract = {

Fluorescent dyes were used to mark and identify the tracks left by extracellular microelectrodes in neurophysiological experiments. Forty-two penetrations were made into the postcentral gyrus of 3 Macaque monkeys with electrodes coated with 1 of 5 fluorescent dyes (Dil, DiO, DiI-C5, PyPO, and Fast Blue). The electrodes were driven at rates ranging from 10 to 1000 \μm/min, to a depth of about 4000 \μm, where a small electrolytic lesion was made. Histological sections were viewed under fluorescent optics and the electrode tracks were reconstructed from the dye traces. Fluorescent traces (width 50\–400 \μm) were observed in 41 of 42 penetrations with 24 traces extending to the lesion site. Of the electrodes driven in less than 3 h, those coated with DiI (88) and DiI-C5 (88) left a trace to the lesion site, while 57\% (47) of the DiO, 40\% (25) of the Fast Blue and only 11\% (19) of the PyPO tracks were fully marked.

This method of marking penetrations can be used with any extracellular recording configuration, does not require tissue sections to be processed or stained, does not require electrical lesions, and causes no detectable tissue damage. Because the dyes fluoresce at different wavelengths, closely spaced tracks can be uniquely identified.

}, keywords = {Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences}, issn = {01650270}, doi = {10.1016/0165-0270(95)00113-1}, url = {https://linkinghub.elsevier.com/retrieve/pii/0165027095001131}, author = {DiCarlo, James J. and Lane, John W. and Hsiao, Steven S. and Johnson, Kenneth O.} } @proceedings {117, title = {Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences}, journal = {Biomedical Engineering Society}, year = {1995}, month = {1995}, address = {Boston, MA}, author = {Hsiao, Steven S. and DiCarlo, James J. and Johnson, Kenneth O} } @proceedings {118, title = {Transformation of tactile spatial form within a cortical column in area 3b of the macaque}, journal = {Society for Neuroscience}, year = {1994}, month = {10/1994}, publisher = {SFN}, address = {Miami, FL}, author = {DiCarlo, James J. and Hsiao, Steven S. and Johnson, Kenneth O} }