Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 57,309 bioRxiv papers from 263,909 authors.

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

By Anthony M Zador

Posted 20 Mar 2019
bioRxiv DOI: 10.1101/582643

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.

Download data

  • Downloaded 6,417 times
  • Download rankings, all-time:
    • Site-wide: 230 out of 57,309
    • In neuroscience: 36 out of 10,055
  • Year to date:
    • Site-wide: 32 out of 57,309
  • Since beginning of last month:
    • Site-wide: 8 out of 57,309

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide

Sign up for the Rxivist weekly newsletter! (Click here for more details.)