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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.
  1. 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

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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Competing Interests

The author declares no competing interests.

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