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NEUROSCIENCE AND MACHINE LEARNING

Bursting potentiates the neuro–AI connection

Nature Neuroscience volume 24, pages 905–906 (2021)Cite this article

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For decades, researchers have wondered whether algorithms used by artificial neural networks might be implemented by biological networks. Payeur et al. have strengthened the connection between neuroscience and artificial intelligence by showing that biologically plausible mechanisms can approximate key features of an essential artificial intelligence learning algorithm.

For many researchers, the connection between neuroscience and artificial intelligence (AI) is a natural one. Over the past several years, artificial neural networks, or ANNs—so-named because of their resemblance to brains—have been trained to perform specific tasks with superhuman performance. For example, artificial systems now routinely beat the best human players at complex games such as Go1 and Dota2, and they can solve enormously complex problems such as protein folding3. Neuroscientists therefore wonder whether these sophisticated systems can teach us something about the mechanisms of cognition in humans and other animals. On the other hand, AI systems are, for the most part, highly specialized. A system designed and trained to play games cannot fold proteins, and vice versa. In the domain of general intelligence, humans still outperform machines. Thus, practitioners of AI wonder whether neuroscience can offer clues as to how to achieve more general artificial intelligence.

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Fig. 1: Does the brain solve the credit assignment problem using learning algorithms akin to back-propagation?.

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Author notes

  1. These authors contributed equally: Weinan Sun, Xinyu Zhao.

Authors and Affiliations

  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA

    Weinan Sun, Xinyu Zhao & Nelson Spruston

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  1. Weinan Sun
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  2. Xinyu Zhao
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  3. Nelson Spruston
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Correspondence to Nelson Spruston.

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Sun, W., Zhao, X. & Spruston, N. Bursting potentiates the neuro–AI connection. Nat Neurosci 24, 905–906 (2021). https://doi.org/10.1038/s41593-021-00844-2

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