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A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains

View ORCID ProfileAnthony M. Zador
doi: https://doi.org/10.1101/582643
Anthony M. Zador
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
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ABSTRACT

Over the last decade, artificial neural networks (ANNs), have undergone a revolution, catalyzed in large part by better tools for supervised learning. However, training such networks requires enormous data sets of labeled examples, whereas young animals (including humans) typically learn with few or no labeled examples. This stark contrast with biological learning has led many in the ANN community posit that instead of supervised paradigms, animals must rely instead primarily on unsupervised learning, leading the search for better unsupervised algorithms. Here we argue that much of an animal’s behavioral repertoire is not the result of clever learning algorithms—supervised or unsupervised—but arises instead from behavior programs already present at birth. These programs arise through evolution, are encoded in the genome, and emerge as a consequence of wiring up the brain. Specifically, animals are born with highly structured brain connectivity, which enables them learn very rapidly. Recognizing the importance of the highly structured connectivity suggests a path toward building ANNs capable of rapid learning.

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  • zador{at}cshl.edu

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 4.0 International license.
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Posted March 20, 2019.
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A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains
Anthony M. Zador
bioRxiv 582643; doi: https://doi.org/10.1101/582643
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A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains
Anthony M. Zador
bioRxiv 582643; doi: https://doi.org/10.1101/582643

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