@conference {187, title = {Learning only a handful of latent variables produces neural-aligned CNN models of the ventral stream}, booktitle = {Computational and Systems Neuroscience (COSYNE) }, year = {2024}, address = {Lisbon, Portugal}, abstract = {

Image-computable modeling of primate ventral stream visual processing has made great strides via brainmapped versions of convolutional neural networks (CNNs) that are optimized on thousands of object categories (ImageNet), the performance of which strongly predicts CNNs\’ neural alignment. However, human and primate visual intelligence extends far beyond object categorization, encompassing a diverse range of tasks, such as estimating the latent variables of object position or pose in the image. The influence of task choice on neural alignment in CNNs, compared to CNN architecture, remains underexplored, partly due to the scarcity of largescale datasets with rich known labels beyond categories. 3D graphic engines, capable of creating training images with detailed information on various latent variables, offer a solution. Here, we asked how the choice of visual tasks that are used to train CNNs (i.e., the set of latent variables to be estimated) affects their ventral stream neural alignment. We focused on the estimation of variables such as object position and pose, and we tested CNNs\’ neural alignment via the Brain-Score open science platform. We found some of these CNNs had neural alignment scores that were very close to those trained on ImageNet, even though their entire training experience has been on synthetic images. Additionally, we found training models on just a handful of latent variables achieved the same level of neural alignment as models trained on a much larger number of categories, suggesting that latent variable training is more efficient than category training in driving model-neural alignment. Moreover, we found that these models\’ neural alignment scores scale with the amount of synthetic data used during training, suggesting the potential of obtaining more aligned models with larger synthetic datasets. This study highlights the effectiveness of using synthetic datasets and latent variables in advancing image-computable models of the ventral visual stream.

}, url = {https://hdl.handle.net/1721.1/153744}, author = {Xie, Yudi and Alter, Esther and Schwartz, Jeremy and DiCarlo, James J} } @article {180, title = {Let{\textquoteright}s move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human visionAbstract}, journal = {Behavioral and Brain Sciences}, volume = {4634}, year = {2023}, month = {Jan-01-2023}, abstract = {

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

}, issn = {0140-525X}, doi = {10.1017/S0140525X23001607}, url = {https://www.cambridge.org/core/product/identifier/S0140525X23001607/type/journal_article}, author = {DiCarlo, James J. and Yamins, Daniel L. K. and Ferguson, Michael E. and Fedorenko, Evelina and Bethge, Matthias and Bonnen, Tyler and Schrimpf, Martin} } @article {13, title = {Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks}, journal = {The Journal of Neuroscience}, volume = {38}, year = {2018}, month = {03/2019}, pages = {7255 - 7269}, abstract = {

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.

}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.0388-18.2018}, url = {http://www.jneurosci.org/content/38/33/7255}, author = {Rajalingham, Rishi and Issa, Elias B. and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.} } @article {21, title = {Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks}, journal = {bioRxiv}, year = {2018}, month = {02/2018}, type = {preprint}, abstract = {

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.

}, doi = {https://doi.org/10.1101/240614}, url = {https://www.biorxiv.org/content/10.1101/240614v4.full.pdf}, author = {Rajalingham, Rishi and Issa, Elias B and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.} } @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.} } @proceedings {84, title = {A large-scale shape map in monkey inferior temporal cortex}, journal = {Society for Neuroscience}, year = {2006}, month = {10/2006}, publisher = {SFN}, address = {Atlanta, GA, USA}, author = {Op de Beeck, H. P. and Deutsch, Jennifer A. and Vanduffel, Wim and Nancy Kanwisher and DiCarlo, James J.} } @article {83, title = {Learning and neural plasticity in visual object recognition}, journal = {Current Opinion in Neurobiology}, volume = {16}, year = {2006}, month = {01/2006}, pages = {152 - 158}, abstract = {

The capability of the adult primate visual system for rapid and accurate recognition of targets in cluttered, natural scenes far surpasses the abilities of state-of-the-art artificial vision systems. Understanding this capability remains a fundamental challenge in visual neuroscience. Recent experimental evidence suggests that adaptive coding strategies facilitated by underlying neural plasticity enable the adult brain to learn from visual experience and shape its ability to integrate and recognize coherent visual objects.

}, issn = {09594388}, doi = {10.1016/j.conb.2006.03.012}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0959438806000377}, author = {Kourtzi, Zoe and DiCarlo, James J} } @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} }