Zador, A. M. (August 2019) A critique of pure learning and what artificial neural networks can learn from animal brains. Nat Commun, 10 (1). p. 3770. ISSN 2041-1723
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Abstract
Artificial neural networks (ANNs) have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals (including humans), training such networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead mainly on unsupervised learning. Here we argue that most animal behavior is not the result of clever learning algorithms-supervised or unsupervised-but is encoded in the genome. Specifically, animals are born with highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is far too complex to be specified explicitly in the genome, it must be compressed through a "genomic bottleneck". The genomic bottleneck suggests a path toward ANNs capable of rapid learning.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics bioinformatics > computational biology > algorithms organism description > animal behavior organs, tissues, organelles, cell types and functions > organs types and functions > brain bioinformatics > computational biology organism description > animal behavior > learning organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks organs, tissues, organelles, cell types and functions > organs types and functions organs, tissues, organelles, cell types and functions |
| CSHL Authors: | |
| Communities: | CSHL labs > Zador lab |
| Depositing User: | Matthew Dunn |
| Date: | 21 August 2019 |
| Date Deposited: | 26 Aug 2019 14:56 |
| Last Modified: | 05 Feb 2024 21:12 |
| PMCID: | PMC6704116 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/38307 |
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