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Showing 1–6 of 6 results
Advanced filters: Author: Kanaka Rajan Clear advanced filters
  • A ‘programming’-like approach provides a one-step algorithm to find network parameters for recurrent neural networks that can model complex dynamical systems.

    • Manuel Beiran
    • Camille A. Spencer-Salmon
    • Kanaka Rajan
    News & Views
    Nature Machine Intelligence
    Volume: 5, P: 570-571
  • The solutions found by neural networks to solve a task are often inscrutable. We have little insight into why a particular structure emerges in a network. By reverse engineering neural networks from dynamical principles, Dubreuil, Valente et al. show how neural population structure enables computational flexibility.

    • Christian David Márton
    • Siyan Zhou
    • Kanaka Rajan
    News & Views
    Nature Neuroscience
    Volume: 25, P: 679-681
  • In mice, a strong aversive experience drives offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed 2 days before, linking fear of the recent aversive memory to the previous neutral memory.

    • Yosif Zaki
    • Zachary T. Pennington
    • Denise J. Cai
    ResearchOpen Access
    Nature
    Volume: 637, P: 145-155
  • Through the study of animal models, translational research aims to uncover mechanisms that may underlie phenomena observed in humans. In this Review, Brynildsen et al. explore the contributions of network science approaches to cross-species translational research in neuroscience.

    • Julia K. Brynildsen
    • Kanaka Rajan
    • Dani S. Bassett
    Reviews
    Nature Reviews Neuroscience
    Volume: 24, P: 575-588