Unsupervised neural network models of the ventral visual stream
By Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins. Proceedings of the National Academy of Sciences of the United States of America, 2020
414 citations recorded by the source corpus.
Significance Primates show remarkable ability to recognize objects. This ability is achieved by their ventral visual stream, multiple hierarchically interconnected brain areas. The best quantitative models of these areas are deep neural networks trained with human annotations. However, they receive more annotations than infants, making them implausible models of the ventral stream development. Here, we report that recent progress in unsupervised learning has largely closed this gap. We find the networks learned with recent unsupervised methods achieve prediction accuracy in the ventral stream that equals or exceeds that of today’s best models. These results illustrate a use of unsupervised learning to model a brain system and present a strong candidate for a biologically plausible computational theory of sensory learning. Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today’s best supervised methods and that the mapping of these neural network models’ hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/e692ab8e6d1b1efb11b24ca31bd9ac53caa021a6
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Citations in the corpus
- SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded From the Infant’s Perspective (2020, background): “” Already, researchers have explored what learning progress is possible when new unsupervised visual learning algorithms are applied to our dataset (Orhan et al., 2020; Zhuang et al., 2020).”
- Targeted perturbations reveal brain-like local coding axes in robustified, but not standard, ANN-based brain models (2025, unknown): “For over a decade, NeuroAI has celebrated artificial neural networks (ANNs) for how well they predict brain responses (Yamins et al., 2014; Kriegeskorte, 2015; Storrs et al., 2021; Zhuang et al., 2021; Doerig et al., 2023).”