%0 Journal Article %J Nature Communications %D 2021 %T Computational models of category-selective brain regions enable high-throughput tests of selectivity %A Murty, NAR %A Bashivan, Pouya %A Abate, Alex %A DiCarlo, James J. %A Kanwisher, Nancy %X

Cortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-responsepredicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.

%B Nature Communications %V 12 %8 Jan-12-2021 %G eng %U https://www.nature.com/articles/s41467-021-25409-6 %N 1 %! Nat Commun %R 10.1038/s41467-021-25409-6 %0 Conference Paper %B Neural Information Processing Systems %D 2019 %T Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs %A Jonas Kubilius %A Martin Schrimpf %A Ha Hong %A Najib Majaj %A Rajalingham, Rishi %A Issa, Elias B. %A Kohitij Kar %A Bashivan, Pouya %A Jonathan Prescott-Roy %A Kailyn Schmidt %A Aran Nayebi %A Daniel Bear %A Daniel L. K. Yamins %A James J. DiCarlo %X

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'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.

%B Neural Information Processing Systems %G eng %U https://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns.pdf %R https://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns %0 Journal Article %J Science %D 2019 %T Neural population control via deep image synthesis %A Bashivan, Pouya %A Kar, Kohitij %A DiCarlo, James J. %X

Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. This method, although not yet perfect, achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today's ANN models might be used to noninvasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.

 

%B Science %V 364 %P eaav9436 %8 03/2019 %G eng %U http://science.sciencemag.org/cgi/rapidpdf/364/6439/eaav9436?ijkey=iBRdlniG7iYuA&keytype=ref&siteid=sci %N 6439 %! Science %R 10.1126/science.aav9436 %0 Conference Paper %B Computational and Systems Neuroscience (COSYNE) %D 2019 %T Using Brain-Score to Evaluate and Build Neural Networks for Brain-Like Object Recognition %A Schrimpf, Martin %A Kubilius, Jonas %A Hong, Ha %A Majaj, Najib %A Rajalingham, Rishi %A Issa, Elias B %A Kar, Kohitij %A Ziemba, Corey M %A Bashivan, Pouya %A Prescott-Roy, Jonathan %A Schmidt, Kailyn %A Yamins, Daniel LK %A DiCarlo, James J %B Computational and Systems Neuroscience (COSYNE) %C Denver, CO %G eng %0 Journal Article %J bioRxiv %D 2018 %T Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? %A Martin Schrimpf %A Kubilius, Jonas %A Ha Hong %A Najib Majaj %A Rajalingham, Rishi %A Issa, Elias B. %A Kar, Kohitij %A Bashivan, Pouya %A Jonathan Prescott-Roy %A Schmidt, Kailyn %A Daniel L. K. Yamins %A DiCarlo, James J. %X

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'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'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.

%B bioRxiv %8 09/2018 %G eng %U https://www.biorxiv.org/content/10.1101/407007v2.full.pdf %9 preprint %R https://doi.org/10.1101/407007 %0 Journal Article %J The Journal of Neuroscience %D 2018 %T Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks %A Rajalingham, Rishi %A Issa, Elias B. %A Bashivan, Pouya %A Kar, Kohitij %A Schmidt, Kailyn %A DiCarlo, James J. %X

Primates-including humans-can typically recognize objects in visual images at a glance even in spite of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials from 1472 anonymous humans and five male macaque monkeys for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNN models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNN models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNN models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks-such as those obtained here-could serve as direct guides for discovering such models.Recently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

%B The Journal of Neuroscience %V 38 %P 7255 - 7269 %8 03/2019 %G eng %U http://www.jneurosci.org/content/38/33/7255 %N 33 %! J. Neurosci. %R 10.1523/JNEUROSCI.0388-18.2018 %0 Journal Article %J bioRxiv %D 2018 %T Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks %A Rajalingham, Rishi %A Issa, Elias B %A Bashivan, Pouya %A Kar, Kohitij %A Schmidt, Kailyn %A DiCarlo, James J. %X

Primates—including humans—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks—such as those obtained here—could serve as direct guides for discovering such models.

%B bioRxiv %8 02/2018 %G eng %U https://www.biorxiv.org/content/10.1101/240614v4.full.pdf %9 preprint %R https://doi.org/10.1101/240614 %0 Journal Article %J arXiv %D 2018 %T Teacher Guided Architecture Search %A Bashivan, Pouya %A Tensen, Mark %A DiCarlo, James J. %X

Much of the recent improvement in neural networks for computer vision has resulted from discovery of new networks architectures. Most prior work has used the performance of candidate models following limited training to automatically guide the search in a feasible way. Could further gains in computational efficiency be achieved by guiding the search via measurements of a high performing network with unknown detailed architecture (e.g. the primate visual system)? As one step toward this goal, we use representational similarity analysis to evaluate the similarity of internal activations of candidate networks with those of a (fixed, high performing) teacher network. We show that adopting this evaluation metric could produce up to an order of magnitude in search efficiency over performance-guided methods. Our approach finds a convolutional cell structure with similar performance as was previously found using other methods but at a total computational cost that is two orders of magnitude lower than Neural Architecture Search (NAS) and more than four times lower than progressive neural architecture search (PNAS). We further show that measurements from only ~300 neurons from primate visual system provides enough signal to find a network with an Imagenet top-1 error that is significantly lower than that achieved by performance-guided architecture search alone. These results suggest that representational matching can be used to accelerate network architecture search in cases where one has access to some or all of the internal representations of a teacher network of interest, such as the brain's sensory processing networks.

%B arXiv %8 04/2018 %G eng %U https://arxiv.org/pdf/1808.01405.pdf %9 preprint %R https://arxiv.org/abs/1808.01405