@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} } @conference {182, title = {Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds}, booktitle = {Conference on Parsimony and Learning (Proceedings Track)}, year = {2023}, address = {Hong Kong, China}, abstract = {

Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches. However, concrete methodologies for bridging the gap between these levels of abstraction remain elusive. In this work, we investigate the internal mechanisms of neural networks through the lens of neural population geometry, aiming to provide understanding at an intermediate level of abstraction, as a way to bridge that gap. Utilizing manifold capacity theory (MCT) from statistical physics and manifold alignment analysis (MAA) from high-dimensional statistics, we probe the underlying organization of task-dependent manifolds in deep neural networks and macaque neural recordings. Specifically, we quantitatively characterize how different learning objectives lead to differences in the organizational strategies of these models and demonstrate how these geometric analyses are connected to the decodability of task-relevant information. These analyses present a strong direction for bridging mechanistic and normative theories in neural networks through neural population geometry, potentially opening up many future research avenues in both machine learning and neuroscience.

}, keywords = {Biologically inspired vision models, computational neuroscience, Neural Geometry, Neural Manifolds, Neuro-AI, Representational Geometry}, url = {https://openreview.net/forum?id=MxBS6aw5Gd}, author = {Michael Kuoch and Chi-Ning Chou and Nikhil Parthasarathy and Joel Dapello and James J. DiCarlo and Haim Sompolinsky and Sueyeon Chung} } @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.} } @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 {14, title = {Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging}, journal = {Biomedical Optics Express}, volume = {9}, year = {2018}, month = {01/2018}, pages = {1492-1509}, abstract = {

A major open challenge in neuroscience is the ability to measure and perturb neural activity in vivo from well defined neural sub-populations at cellular resolution anywhere in the brain. However, limitations posed by scattering and absorption prohibit non-invasive multi-photon approaches for deep (\>2mm) structures, while gradient refractive index (GRIN) endoscopes are relatively thick and can cause significant damage upon insertion. Here, we present a novel micro-endoscope design to image neural activity at arbitrary depths via an ultra-thin multi-mode optical fiber (MMF) probe that has 5-10X thinner diameter than commercially available micro-endoscopes. We demonstrate micron-scale resolution, multi-spectral and volumetric imaging. In contrast to previous approaches, we show that this method has an improved acquisition speed that is sufficient to capture rapid neuronal dynamics in-vivo in rodents expressing a genetically encoded calcium indicator (GCaMP). Our results emphasize the potential of this technology in neuroscience applications and open up possibilities for cellular resolution imaging in previously unreachable brain regions.

}, issn = {2156-7085}, doi = {10.1364/BOE.9.001492}, url = {https://www.osapublishing.org/abstract.cfm?URI=boe-9-4-1492}, author = {Ohayon, Shay and Caravaca-Aguirre, Antonio and Piestun, Rafael 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} } @article {34, title = {Large-Scale, High-Resolution Neurophysiological Maps Underlying fMRI of Macaque Temporal Lobe}, journal = {Journal of Neuroscience}, volume = {33}, year = {2013}, month = {2013}, pages = {15207 - 15219}, abstract = {

Maps obtained by functional magnetic resonance imaging (fMRI) are thought to reflect the underlying spatial layout of neural activity. However, previous studies have not been able to directly compare fMRI maps to high-resolution neurophysiological maps, particularly in higher level visual areas. Here, we used a novel stereo microfocal x-ray system to localize thousands of neural recordings across monkey inferior temporal cortex (IT), construct large-scale maps of neuronal object selectivity at subvoxel resolution, and compare those neurophysiology maps with fMRI maps from the same subjects. While neurophysiology maps contained reliable structure at the sub-millimeter scale, fMRI maps of object selectivity contained information at larger scales (\>2.5 mm) and were only partly correlated with raw neurophysiology maps collected in the same subjects. However, spatial smoothing of neurophysiology maps more than doubled that correlation, while a variety of alternative transforms led to no significant improvement. Furthermore, raw spiking signals, once spatially smoothed, were as predictive of fMRI maps as local field potential signals. Thus, fMRI of the inferior temporal lobe reflects a spatially low-passed version of neurophysiology signals. These findings strongly validate the widespread use of fMRI for detecting large (\>2.5 mm) neuronal domains of object selectivity but show that a complete understanding of even the most pure domains (e.g., faces vs nonface objects) requires investigation at fine scales that can currently only be obtained with invasive neurophysiological methods.

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.1248-13.2013}, url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.1248-13.2013}, author = {Issa, E. B. and Papanastassiou, A. M. and DiCarlo, J. 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} } @article {35, title = {Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons}, journal = {PLoS Computational Biology}, volume = {9}, year = {2013}, month = {08/2013}, pages = {e1003167}, abstract = {

The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e., represent conceptual classes such as animate and inanimate objects). In this study, we investigated to what extent semantic, rather than purely visual information, is represented in IT by performing a multivariate analysis of IT responses to a set of visual objects. By relying on a variety of machine-learning approaches (including a cutting-edge clustering algorithm that has been recently developed in the domain of statistical physics), we found that, in most instances, IT representation of visual objects is accounted for by their similarity at the level of shape or, more surprisingly, low-level visual properties. Only in a few cases we observed IT representations of semantic classes that were not explainable by the visual similarity of their members. Overall, these findings reassert the primary function of IT as a conveyor of explicit visual shape information, and reveal that low-level visual properties are represented in IT to a greater extent than previously appreciated. In addition, our work demonstrates how combining a variety of state-of-the-art multivariate approaches, and carefully estimating the contribution of shape similarity to the representation of object categories, can substantially advance our understanding of neuronal coding of visual objects in cortex.

}, doi = {10.1371/journal.pcbi.1003167}, url = {https://dx.plos.org/10.1371/journal.pcbi.1003167}, author = {Baldassi, Carlo and Alemi-Neissi, Alireza and Pagan, Marino and DiCarlo, James J. and Zecchina, Riccardo and Zoccolan, Davide} } @proceedings {43, title = {Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks}, journal = {IEEE Workshop on Applications of Computer Vision (WACV)}, year = {2011}, month = {01/2011}, pages = {463-470}, publisher = {IEEE}, address = {Kona, Hawaii, USA}, abstract = {

Tolerance (\“invariance\”) to identity-preserving image variation (e.g. variation in position, scale, pose, illumination) is a fundamental problem that any visual object recognition system, biological or engineered, must solve. While standard natural image database benchmarks are useful for guiding progress in computer vision, they can fail to probe the ability of a recognition system to solve the invariance problem. Thus, to understand which computational approaches are making progress on solving the invariance problem, we compared and contrasted a variety of state-of-the-art visual representations using synthetic recognition tasks designed to systematically probe invariance. We successfully re-implemented a variety of state-of-the-art visual representations and confirmed their published performance on a natural image benchmark. We here report that most of these representations perform poorly on invariant recognition, but that one representation shows significant performance gains over two baseline representations. We also show how this approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition.

}, issn = {978-1-4244-9497-2}, doi = {10.1109/WACV.2011.5711540}, url = {https://ieeexplore.ieee.org/document/5711540}, author = {Pinto, Nicolas and Barhomi, Y and Cox, David D. and DiCarlo, James J.} } @proceedings {44, title = {From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex}, journal = {Computational and Systems Neuroscience (COSYNE)}, year = {2011}, month = {02/2011}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, abstract = {

In primates, visual object information is processed through a hierarchy of cortico-cortical stages that culminates with the inferotemporal cortex (IT). Although the nature of visual processing in IT is still poorly understood, several lines of evidence suggest that IT conveys an explicit object representation that can directly serve as a basis for decision, action and memory - e.g., it can support flexible formation of semantic categories in downstream areas, such as prefrontal and perirhinal cortex. However, some recent studies (Kiani et al, 2007; Kriegeskorte et al, 2008) have argued that IT neuronal ensembles may themselves code the semantic membership of visual objects (i.e., represent such abstract conceptual classes such as animate and inanimate objects, animals, etc). In this study, we have applied an array of multi-variate computational approaches to investigate the nature of visual objects\&$\#$39; representation in IT. Our results show that IT neuronal ensembles represent a surprisingly broad spectrum of visual features complexity, ranging from low-level visual properties (e.g., brightness), to visual patterns of intermediate complexity (e.g., star-like shapes), to complex objects (e.g., four-leg animals) that appear to be coded so invariantly that their clustering in the IT neuronal space is not easily accountable by any similarity metric we used. On the one hand, these findings show that IT supports recognition of low-level properties of the visual input that are typically though to be extracted by lower-level visual areas. On the other hand, IT appears to convey such an explicit representation of some object classes that coding of semantic membership in IT (at least for a few categories) cannot be excluded. Overall, these results shed new light on IT amazing pluripotency in supporting recognition tasks as diverse as detection of brightness and categorization of complex shapes.

}, url = {http://www.cosyne.org/c/index.php?title=Cosyne_11_posters/I-89}, author = {Pagan, Marino and Neissi, AA and Baldassi, Carlo and Zecchina, Riccardo and DiCarlo, James J. and Zoccolan, Davide} } @proceedings {52, title = {A High-Throughput Screening Approach to Biologically-Inspired Object Recognition}, journal = {Learning Workshop-Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {03/2010}, address = {Snow Bird, Utah, USA}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @proceedings {51, title = {Human versus machine: comparing visual object recognition systems on a level playing field}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {02/2010}, address = {Salt Lake City, Utah, USA}, abstract = {

It is received wisdom that biological visual systems easily outmatch current artificial systems at complex visual tasks like object recognition. But have the appropriate comparisons been made? Because artificial systems are improving every day, they may surpass human performance some day. We must understand our progress toward reaching that day, because that success is one of several necessary requirements for \"understanding\" visual object recognition. How large (or small) is the difference in performance between current state-of-the-art object recognition systems and the primate visual system? In practice, the performance comparison of any two object recognition systems requires a focus on the computational crux of the problem and sets of images that engage it. Although it is widely believed that tolerance (\"invariance\") to identity-preserving image variation (e.g. variation in object position, scale, pose, illumination) is critical, systematic comparisons of state-of-the-art artificial visual representations almost always rely on \"natural\" image databases that can fail to probe the ability of a recognition system to solve the invariance problem [Pinto et al PLoS08, COSYNE08, ECCV08, CVPR09]. Thus, to understand how well current state-of-the-art visual representations perform relative to each other, relative to low-level neuronal representations (e.g. retinal-like and V1-like), and relative to high-level representations (e.g. human performance), we tested all of these representations on a common set of visual object recognition tasks that directly engage the invariance problem. Specifically, we used a synthetic testing approach that allows direct engagement of the invariance problem, as well as knowledge and control of all the key parameters that make object recognition challenging. We successfully re-implemented a variety of state-of-the-art visual representations, and we confirmed the high published performance of all of these state-of-the-art representations on large, complex \"natural\â\€ image benchmarks. Surprisingly, we found that most of these representations were weak on our simple synthetic tests of invariant recognition, and only high-level biologically-inspired representations showed performance gains above the neuroscience \"null\" representation (V1-like). While in aggregate, we found that the performance of these state-of-the-art representations pales in comparison to human performance, humans and computers seem to fail in different and potentially enlightening ways when faced with the problem of invariance. We also show how our synthetic testing approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition.

}, doi = {10.3389/conf.fnins.2010.03.00283}, url = {https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00283\&eid=770\&sname=Computational_and_Systems_Neuroscience_2010}, author = {Pinto, Nicolas and Majaj, Najib J and Barhomi, Y and Solomon, E. A. and Cox, David D. and DiCarlo, James J.} } @proceedings {48, title = {Towards large-scale, high resolution maps of object selectivity in inferior temporal cortex}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {02/2010}, address = {Salt Lake City, Utah, USA}, abstract = {

Inferior temporal cortex (IT) has been shown to have large-scale (mm to cm) maps of object category selectivity as well as small-scale (sub-millimeter) organization for object features.\  These two scales of spatial organization have yet to be linked because they were measured using different techniques (fMRI, optical imaging, and local electrophysiology), each with their own limitations.\  For example, fMRI has poor spatial resolution while optical imaging has higher resolution at the expense of a narrow field of view of only surface-accessible cortex.\  Given that much of IT lies inside a major cortical sulcus or at the skull base, what is needed is a method that can access the whole of IT with high resolution.\  Microelectrode based mapping has such potential: electrodes can reach almost anywhere in IT (high spatial coverage) and record from single cells (high spatial resolution).\ This potential has not yet been realized because of the difficulty of precisely localizing and co-registering many electrode recordings in vivo.\  Methods such as histological reconstruction of lesion sites or MRI visualization of electrodes are post-hoc and can introduce additional spatial errors.\  Here, we have adopted a microfocal stereo x-ray system for localizing electrodes that can be used at an unlimited number of sites and operates virtually in real-time (Cox et al., J. Neurophys. 2008).\  We have used this system to construct broad scale maps of object category selectivity in IT for comparison to fMRI-based maps.\  We found a weak but significant correspondence between physiology and fMRI maps collected in the same animal, and this correspondence improved substantially when MUA and LFP signals were smoothed (~3-5 mm) to broader scales suggesting the spatial low pass nature of fMRI.\  Transformations other than spatial smoothing such as dividing the LFP into power in different frequency bands did not produce noticeable improvement in map correspondence.

}, doi = {10.3389/conf.fnins.2010.03.00154}, url = {https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00154\&eid=770\&sname=computational_and_systems_neur}, author = {Issa, Elias B. and Papanastassiou, A. M. and Andken, BB and DiCarlo, James J.} } @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.} } @conference {57, title = {How far can you get with a modern face recognition test set using only simple features?}, booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops)}, year = {2009}, month = {06/2009}, publisher = {IEEE}, organization = {IEEE}, address = {Miami, FL}, abstract = {

In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance \“bar\” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields \“state-of-the-art\” performance levels on a standard face recognition set (\“labeled faces in the wild\”). However, at the same time, even with the inclusion of MKL techniques, systems based on these simple features still fail on a synthetic face recognition test that includes more \“realistic\” view variation by design. These results underscore the importance of building test sets focussed on capturing the central computational challenges of real-world face recognition.

}, isbn = {978-1-4244-3992-8}, doi = {10.1109/CVPR.2009.5206605}, url = {https://ieeexplore.ieee.org/document/5206605}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @proceedings {66, title = {A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe}, journal = {Society for Neuroscience}, year = {2009}, month = {11/2009}, publisher = {SFN}, address = {Washington, DC, USA}, author = {Papanastassiou, A. M. and Op de Beeck, H. P. and Andken, BB and DiCarlo, James J.} } @proceedings {60, title = {Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach}, journal = {NVIDIA GPU Technology Conference}, year = {2009}, month = {2009}, author = {Pinto, Nicolas and Cox, David D. and DiCarlo, James J.} } @proceedings {61, title = {Unlocking Brain-Inspired Computer Vision}, journal = {GPU@BU}, year = {2009}, month = {2009}, author = {Pinto, Nicolas and Cox, David D. and DiCarlo, James J.} } @proceedings {62, title = {The Visual Cortex and GPUs}, journal = {GPU Computing for Biomedical Research}, year = {2009}, month = {2009}, address = {Massachusetts General Hospital, Boston, MA}, author = {Pinto, Nicolas and Cox, David D. and DiCarlo, James J.} } @conference {70, title = {Establishing Good Benchmarks and Baselines for Face Recognition}, booktitle = {European Conference on Computer Vision-Faces in {\textquoteright}Real-Life{\textquoteright} Images Workshop}, year = {2008}, month = {10/2008}, publisher = {EECV}, organization = {EECV}, address = {Marseille, France}, abstract = {

Progress in face recognition relies critically on the creation of test sets against which the performance of various approaches can be evaluated. A good set must capture the essential elements of what makes the problem hard, while conforming to practical scale limitations. However, these goals are often deceptively difficult to achieve. In the related area of object recognition, Pinto et al. [2] demonstrated the potential dangers of using a large, uncontrolled natural image set, showing that an extremely rudimentary vision system (inspired by the early stages of visual processing in the brain) was able to perform on par with many state-of-the-art vision systems on the popular Caltech101 object set [3].\  At the same time, this same rudimentary system was easily defeated by an ostensibly \"simpler\" synthetic recognition test designed to better span the range of real world variation in object pose, position, scale, etc. These results suggested that image sets that look \"natural\" to human observers may nonetheless fail to properly embody the problem of interest, and that care must be taken to establish baselines against which performance can be judged. Here, we repeat this approach for the \"LabeledFaces in the Wild\" (LFW) dataset [1], and for a collection of standard face recognition tests. The goal of the present work is not to compete in the LFW challenge, per se, but to provide a baseline against which the performance of other systems can be judged. In particular, we found that our rudimentary \"baseline\" vision system was able to achieve 68\% correct performance on the LFW challenge, substantially higher than a pure chance\" baseline. We argue that this value might serve as a more useful baseline against which to evaluate absolute performance and argue that the LFW set, while perhaps not perfect, represents an improvement over other standard face sets.

}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @article {54, title = {Fine-Scale Spatial Organization of Face and Object Selectivity in the Temporal Lobe: Do Functional Magnetic Resonance Imaging, Optical Imaging, and Electrophysiology Agree?}, journal = {Journal of Neuroscience}, volume = {28}, year = {2008}, month = {12/2008}, pages = {11796 - 11801}, abstract = {

The spatial organization of the brain\&$\#$39;s object and face representations in the temporal lobe is critical for understanding high-level vision and cognition but is poorly understood. Recently, exciting progress has been made using advanced imaging and physiology methods in humans and nonhuman primates, and the combination of such methods may be particularly powerful. Studies applying these methods help us to understand how neuronal activity, optical imaging, and functional magnetic resonance imaging signals are related within the temporal lobe, and to uncover the fine-grained and large-scale spatial organization of object and face representations in the primate brain.

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.3799-08.2008}, url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3799-08.2008}, author = {Op de Beeck, H. P. and DiCarlo, J. J. and Goense, J. B. M. and Grill-Spector, K. and Papanastassiou, A. and Tanifuji, M. and Tsao, D. Y.} } @article {64, title = {High-Resolution Three-Dimensional Microelectrode Brain Mapping Using Stereo Microfocal X-ray Imaging}, journal = {Journal of Neurophysiology}, volume = {100}, year = {2008}, month = {01/2008}, pages = {2966 - 2976}, abstract = {

Much of our knowledge of brain function has been gleaned from studies using microelectrodes to characterize the response properties of individual neurons in vivo. However, because it is difficult to accurately determine the location of a microelectrode tip within the brain, it is impossible to systematically map the fine three-dimensional spatial organization of many brain areas, especially in deep structures. Here, we present a practical method based on digital stereo microfocal X-ray imaging that makes it possible to estimate the three-dimensional position of each and every microelectrode recording site in \"real time\" during experimental sessions. We determined the system\&$\#$39;s ex vivo localization accuracy to be better than 50 microm, and we show how we have used this method to coregister hundreds of deep-brain microelectrode recordings in monkeys to a common frame of reference with median error of {\textless}150 microm. We further show how we can coregister those sites with magnetic resonance images {(MRIs),} allowing for comparison with anatomy, and laying the groundwork for more detailed electrophysiology/functional {MRI} comparison. Minimally, this method allows one to marry the single-cell specificity of microelectrode recording with the spatial mapping abilities of imaging techniques; furthermore, it has the potential of yielding fundamentally new kinds of high-resolution maps of brain function.

}, issn = {0022-3077}, doi = {10.1152/jn.90672.2008}, url = {https://www.physiology.org/doi/10.1152/jn.90672.2008}, author = {Cox, David D. and Papanastassiou, Alexander M. and Oreper, Daniel and Andken, Benjamin B. and DiCarlo, James J.} } @proceedings {71, title = {A high-throughput screening approach to discovering good forms of visual representation}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, address = {Salt Lake City, Utah, USA}, author = {Cox, David D. and Pinto, Nicolas and Doukhan, David and Corda, B and DiCarlo, James J.} } @proceedings {77, title = {Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, publisher = {COSYNE}, address = {Salt Lake City, Utah, USA}, author = {Pinto, Nicolas and Cox, David D. and Corda, B and Doukhan, David and DiCarlo, James 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} } @article {78, title = {Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex}, journal = {Journal of Neuroscience}, volume = {27}, year = {2007}, month = {07/2007}, pages = {12292 - 12307}, abstract = {

Object recognition requires both selectivity among different objects and tolerance to vastly different retinal images of the same object, resulting from natural variation in (e.g.) position, size, illumination, and clutter. Thus, discovering neuronal responses that have object selectivity and tolerance to identity-preserving transformations is fundamental to understanding object recognition. Although selectivity and tolerance are found at the highest level of the primate ventral visual stream [the inferotemporal cortex {(IT)],} both properties are highly varied and poorly understood. If an {IT} neuron has very sharp selectivity for a unique combination of object features (\"diagnostic features\"), this might automatically endow it with high tolerance. However, this relationship cannot be taken as given; although some {IT} neurons are highly object selective and some are highly tolerant, the empirical connection of these key properties is unknown. In this study, we systematically measured both object selectivity and tolerance to different identity-preserving image transformations in the spiking responses of a population of monkey {IT} neurons. We found that {IT} neurons with high object selectivity typically have low tolerance (and vice versa), regardless of how object selectivity was quantified and the type of tolerance examined. The discovery of this trade-off illuminates object selectivity and tolerance in {IT} and unifies a range of previous, seemingly disparate results. This finding also argues against the idea that diagnostic conjunctions of features guarantee tolerance. Instead, it is naturally explained by object recognition models in which object selectivity is built through {AND-like} tuning mechanisms.

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.1897-07.2007}, url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.1897-07.2007}, author = {Zoccolan, D. and Kouh, M. and Poggio, T. and DiCarlo, J. 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.} } @proceedings {86, title = {Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex}, journal = {Gordon Conference: Sensation and the Natural Environment}, year = {2006}, month = {2006}, address = {Bozeman, MT, USA}, author = {Zoccolan, Davide and Kouh, M 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.} }