Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Choose more journals

Article type Check one or more article types to show results from those article types only.
Subject Check one or more subjects to show results from those subjects only.
Date Choose a date option to show results from those dates only.

Custom date range

Clear all filters
Sort by:
Showing 1–7 of 7 results
Advanced filters: Author: Danijela Marković Clear advanced filters

  • Methods to train physical neural networks, such as backpropagation-based and backpropagation-free approaches, are explored to allow scaling up of artificial intelligence models far beyond present small-scale laboratory demonstrations, potentially enhancing computational efficiency.

    • Ali Momeni
    • Babak Rahmani
    • Romain Fleury
    Reviews
    Nature
    Volume: 645, P: 53-61

  • Integration of memristors in a chain of nano-constriction spintronic oscillators allows for individual control of oscillation frequencies and emerging synchronization patterns. The control of such synchronization could enable learning through association like neurons in the brain.

    • Danijela Marković
    News & Views
    Nature Materials
    Volume: 21, P: 4-5

  • Spintronic nano-neurons and synapses can be connected by radiofrequency signals into neural networks that are capable of classifying real-world radiofrequency inputs without digitization at high speed and with low energy costs—an important step for artificial intelligence at the edge.

    • Andrew Ross
    • Nathan Leroux
    • Julie Grollier
    Research
    Nature Nanotechnology
    Volume: 18, P: 1273-1280

  • Ising machines have been usually applied to predefined combinatorial problems due to their distinct physical properties. The authors introduce an approach that utilizes equilibrium propagation for the training of Ising machines and achieves high accuracy performance on classification tasks.

    • Jérémie Laydevant
    • Danijela Marković
    • Julie Grollier
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-14

  • Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Including more physics in the algorithms and nanoscale materials used for computing could have a major impact in this field.

    • Danijela Marković
    • Alice Mizrahi
    • Julie Grollier
    Reviews
    Nature Reviews Physics
    Volume: 2, P: 499-510