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Neural computation

A ‘programming’ framework for recurrent neural networks

Nature Machine Intelligence volume 5, pages 570–571 (2023)Cite this article

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A ‘programming’-like approach provides a one-step algorithm to find network parameters for recurrent neural networks that can model complex dynamical systems.

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Fig. 1: Programming of a recurrent neural network.

References

  1. Larsen, B. W. & Druckmann, S. PLoS Comput. Biol. 18, e1010227 (2022).

    Article  Google Scholar 

  2. Kim, J. Z. & Bassett, D. S. Nat. Mach. Intell. https://doi.org/10.1038/s42256-023-00668-8 (2023).

  3. Doya, K. IEEE Transactions on Neural Networks 1, 1–6 (1993).

    Google Scholar 

  4. Maass, W., Joshi, P. & Sontag, E. D. PLoS Comput. Biol. 3, e165 (2007).

    Article  Google Scholar 

  5. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (ICLR) (ICLR, 2015)

  6. Paszke, A. et al. NIPS 2017 Autodiff Workshop (2017).

  7. Eliasmith, C. & Anderson, C. H. Neural engineering: Computation, representation, and dynamics in neurobiological systems. (MIT Press, 2003).

  8. Beiran, M., Dubreuil, A., Valente, A., Mastrogiuseppe, F. & Ostojic, S. Neural Comput. 33, 1572–1615 (2021).

    Article  MathSciNet  Google Scholar 

  9. Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T. & Wang, X.-J. Nat. Neurosci. 22, 297–306 (2019).

    Article  Google Scholar 

  10. Márton, C. D., Gagnon, L., Lajoie, G. & Rajan, K. Preprint at arXiv https://doi.org/10.48550/arXiv.2105.14108 (2021).

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Authors and Affiliations

  1. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Manuel Beiran

  2. Nash Family Department of Neuroscience, Icahn School of Medicine, Mount Sinai, New York, NY, USA

    Manuel Beiran, Camille A. Spencer-Salmon & Kanaka Rajan

Authors
  1. Manuel Beiran
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  2. Camille A. Spencer-Salmon
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  3. Kanaka Rajan
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Corresponding authors

Correspondence to Manuel Beiran or Kanaka Rajan.

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The authors declare no competing interests.

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Beiran, M., Spencer-Salmon, C.A. & Rajan, K. A ‘programming’ framework for recurrent neural networks. Nat Mach Intell 5, 570–571 (2023). https://doi.org/10.1038/s42256-023-00674-w

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