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A critique of pure learning and what artificial neural networks can learn from animal brains

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  • Anthony M. Zador

    (Cold Spring Harbor Laboratory)

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.

Suggested Citation

  • Anthony M. Zador, 2019. "A critique of pure learning and what artificial neural networks can learn from animal brains," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11786-6
    DOI: 10.1038/s41467-019-11786-6
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    Cited by:

    1. Joachim A Holst-Hansen & Carsten Bergenholtz, 2020. "Does the size of rewards influence performance in cognitively demanding tasks?," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    2. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
    3. Federico Bolaños & Javier G. Orlandi & Ryo Aoki & Akshay V. Jagadeesh & Justin L. Gardner & Andrea Benucci, 2024. "Efficient coding of natural images in the mouse visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Bao, Han & Yu, Xihong & Zhang, Yunzhen & Liu, Xiaofeng & Chen, Mo, 2023. "Initial condition-offset regulating synchronous dynamics and energy diversity in a memristor-coupled network of memristive HR neurons," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    5. Francesco Poli & Yi-Lin Li & Pravallika Naidu & Rogier B. Mars & Sabine Hunnius & Azzurra Ruggeri, 2024. "Toddlers strategically adapt their information search," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    6. Barbara Feulner & Matthew G. Perich & Raeed H. Chowdhury & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2022. "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    8. Dániel L. Barabási & Gregor F. P. Schuhknecht & Florian Engert, 2024. "Functional neuronal circuits emerge in the absence of developmental activity," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    9. Bossert, Leonie & Hagendorff, Thilo, 2021. "Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation," Technology in Society, Elsevier, vol. 67(C).
    10. Alexander Ororbia & Daniel Kifer, 2022. "The neural coding framework for learning generative models," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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