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Complex computation from developmental priors

Author

Listed:
  • Dániel L. Barabási

    (Harvard University)

  • Taliesin Beynon

    (Wolfram Physics Project)

  • Ádám Katona

    (Imperial College London)

  • Nicolas Perez-Nieves

    (University of York)

Abstract

Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Here, we derive a neurodevelopmental encoding of artificial neural networks that considers the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the network’s weights directly, we improve task fitness by updating the neurons’ wiring rules, thereby mirroring evolutionary selection on brain development. We find that our model (1) provides sufficient representational power for high accuracy on ML benchmarks while also compressing parameter count, and (2) can act as a regularizer, selecting simple circuits that provide stable and adaptive performance on metalearning tasks. In summary, by introducing neurodevelopmental considerations into ML frameworks, we not only model the emergence of innate behaviors, but also define a discovery process for structures that promote complex computations.

Suggested Citation

  • Dániel L. Barabási & Taliesin Beynon & Ádám Katona & Nicolas Perez-Nieves, 2023. "Complex computation from developmental priors," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37980-1
    DOI: 10.1038/s41467-023-37980-1
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    References listed on IDEAS

    as
    1. Daniel Witvliet & Ben Mulcahy & James K. Mitchell & Yaron Meirovitch & Daniel R. Berger & Yuelong Wu & Yufang Liu & Wan Xian Koh & Rajeev Parvathala & Douglas Holmyard & Richard L. Schalek & Nir Shavi, 2021. "Connectomes across development reveal principles of brain maturation," Nature, Nature, vol. 596(7871), pages 257-261, August.
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