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Unsupervised Neural Network Models of the Ventral Visual Stream

Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins
doi: https://doi.org/10.1101/2020.06.16.155556
Chengxu Zhuang
1Stanford University, Stanford, USA
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  • For correspondence: chengxuz@stanford.edu
Siming Yan
2The University of Texas at Austin, Austin, USA
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Aran Nayebi
1Stanford University, Stanford, USA
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Martin Schrimpf
3Massachusetts Institute of Technology, Cambridge, USA
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Michael C. Frank
1Stanford University, Stanford, USA
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James J. DiCarlo
3Massachusetts Institute of Technology, Cambridge, USA
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Daniel L. K. Yamins
1Stanford University, Stanford, USA
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Abstract

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. Moreover, we find that these methods produce brain-like representations even when trained on noisy and limited data measured from real children’s developmental experience. We also find that semi-supervised deep contrastive embeddings can leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results suggest that deep contrastive embedding objectives may be a biologically-plausible computational theory of primate visual development.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 18, 2020.
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Unsupervised Neural Network Models of the Ventral Visual Stream
Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins
bioRxiv 2020.06.16.155556; doi: https://doi.org/10.1101/2020.06.16.155556
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Unsupervised Neural Network Models of the Ventral Visual Stream
Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins
bioRxiv 2020.06.16.155556; doi: https://doi.org/10.1101/2020.06.16.155556

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