How AI could lead to a better understanding of the brain

Jain, Viren

doi:10.1038/d41586-023-03426-3

COMMENT
07 November 2023

How AI could lead to a better understanding of the brain

Early machine-learning systems were inspired by neural networks — now AI might allow neuroscientists to get to grips with the brain’s unique complexities.

Viren Jain⁰

Viren Jain
1. Viren Jain is a senior staff research scientist at Google Research, 1600 Amphitheatre Parkway, Mountain View, California, and leads the Connectomics at Google team.
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A multi-coloured visualisation of all incoming connections onto a single human pyramidal neuron in cerebral cortex — A reconstruction of incoming connections to a single human neuron, called a pyramidal cell. Credit: A. Shapson-Coe, M. Januszewski, D. Berger, A. Pope, V. Jain & J. Lichtman

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Can a computer be programmed to simulate a brain? It’s a question mathematicians, theoreticians and experimentalists have long been asking — whether spurred by a desire to create artificial intelligence (AI) or by the idea that a complex system such as the brain can be understood only when mathematics or a computer can reproduce its behaviour. To try to answer it, investigators have been developing simplified models of brain neural networks since the 1940s¹. In fact, today’s explosion in machine learning can be traced back to early work inspired by biological systems.

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Nature 623, 247-250 (2023)

doi: https://doi.org/10.1038/d41586-023-03426-3

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The author declares no competing interests.

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[1] McCulloch, W. S. & Pitts, W. Bull. Math. Biophys. 5, 115–133 (1943).
Article Google Scholar

[2] Jefferis, G., Collinson, L., Bosch, C., Costa, M. & Schlegel, P. Scaling up Connectomics (Wellcome, 2023).
Google Scholar

[3] Cook, S. J. et al. Nature 571, 63–71 (2019).
Article PubMed Google Scholar

[4] Winding, M. et al. Science 379, eadd9330 (2023).
Article PubMed Google Scholar

[5] Dorkenwald, S. et al. Preprint at bioRxiv https://doi.org/10.1101/2023.06.27.546656 (2023).

[6] De Ceglia, R. et al. Nature 622, 120–129 (2023).
Article PubMed Google Scholar

[7] Schretter, C. E. et al. eLife 9, e58942 (2020).
Article PubMed Google Scholar

[8] Wilson, R. I. Annu. Rev. Neurosci. 46, 403–423 (2023).
Article PubMed Google Scholar

[9] Wanner, A. A. & Friedrich, R. W. Nature Neurosci. 23, 433–442 (2020).
Article PubMed Google Scholar

[10] Vishwanathan, A. et al. Preprint at bioRxiv https://doi.org/10.1101/2020.10.28.359620 (2020).

[11] Petrucco, L. et al. Nature Neurosci. 26, 765–773 (2023).
Article PubMed Google Scholar

[12] Chen, X. et al. Neuron 100, 876–890 (2018).
Article PubMed Google Scholar

[13] Takemura, S.-Y. et al. Nature 500, 175–181 (2013).
Article PubMed Google Scholar

[14] Kilman, V. L. & Marder, E. J. Comp. Neurol. 374, 362–375 (1996).
Article PubMed Google Scholar

[15] Bargmann, C. I. & Marder, E. Nature Methods 10, 483–490 (2013).
Article PubMed Google Scholar

[16] Lam, R. et al. Preprint at https://arxiv.org/abs/2212.12794 (2023).

[17] Espeholt, L. et al. Nature Commun. 13, 5145 (2022).
Article PubMed Google Scholar

[18] Witvliet, D. et al. Nature 596, 257–261 (2021).
Article PubMed Google Scholar

[19] Haspel, G. et al. Preprint at https://arxiv.org/abs/2308.06578 (2023).

[20] Frégnac, Y. & Laurent, G. Nature 513, 27–29 (2014).
Article PubMed Google Scholar

How AI could lead to a better understanding of the brain

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