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Showing 1–12 of 12 results
Advanced filters: Author: Kathy Lüdge Clear advanced filters

  • Light-based devices can reduce the energy consumption of computers, but most rely on lasers, which are expensive to integrate with other technologies. An approach that uses LEDs instead of lasers provides a path forwards.

    • Kathy Lüdge
    • Lina Jaurigue
    News & Views
    Nature
    Volume: 632, P: 34-35

  • Picco et al experimentally validate an optimisation method for reservoir computers in which a delayed version of the input is provided. Their approach, validated on several benchmarks, improves performance and facilitates hyperparameter tuning.

    • Enrico Picco
    • Lina Jaurigue
    • Serge Massar
    ResearchOpen Access
    Communications Engineering
    Volume: 4, P: 1-9

  • The capacity of brain networks to retain information during aging is crucial but not yet known. This study shows that the brain’s memory capacity, modelled with reservoir computing, offers new insights into aging, brain function and cognitive decline.

    • Mite Mijalkov
    • Ludvig Storm
    • Joana B. Pereira
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-14

  • Quantum coherence effects are often only visible at low temperatures, where dephasing times are not too short. Here Kolarczik et al. show that quantum coherence effects can appear in the reshaping of ultrafast laser pulses passing through quantum dots even at room temperature.

    • Mirco Kolarczik
    • Nina Owschimikow
    • Ulrike Woggon
    ResearchOpen Access
    Nature Communications
    Volume: 4, P: 1-7

  • Photonic computing devices are a compelling alternative to conventional computing setups for machine learning applications, as they are nonlinear, fast and easy to parallelize. Recent work demonstrates the potential of these optical systems to process and classify human motion from video.

    • Kathy Lüdge
    • André Röhm
    News & Views
    Nature Machine Intelligence
    Volume: 1, P: 551-552

  • Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.

    • Lina Jaurigue
    • Kathy Lüdge
    Comments & OpinionOpen Access
    Nature Communications
    Volume: 13, P: 1-3

  • The synchronisation of optical cavities in the quantum regime is still relatively unexplored. Here, Kreinberg et al. investigate the coupling behaviour of two quantum dot microlasers and find that spontaneous emission noise from cavity QED effects plays an important role.

    • Sören Kreinberg
    • Xavier Porte
    • Stephan Reitzenstein
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-11

  • VCSEL-based photonic Spiking Neural Networks (p-SNNs) have achieved very good performance on classification tasks, however the lack of internal memory has prevented their use in predictive tasks. Here the authors introduce a p-SNN with added memory capacity and show that it can predict a chaotic time series with excellent accuracy

    • Dafydd Owen-Newns
    • Lina Jaurigue
    • Antonio Hurtado
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-9

  • The authors numerically investigate the reservoir computing performance of vertical emitting two-mode semiconductor lasers and show the crucial impact of dynamic coupling, injection schemes and system timescales. A central finding is that high dimensional internal dynamics can only be utilized if an appropriate perturbation via the input is chosen.

    • Lukas Mühlnickel
    • Jonnel A. Jaurigue
    • Kathy Lüdge
    ResearchOpen Access
    Communications Physics
    Volume: 7, P: 1-12

  • Jonnel Jaurigue and co-authors improve the performance of reservoir computing by training on past node states. Their multi-random-timeshifting method can be translated to physical reservoir readout data as showcased in the experimental demonstration.

    • Jonnel Jaurigue
    • Joshua Robertson
    • Kathy Lüdge
    ResearchOpen Access
    Communications Engineering
    Volume: 4, P: 1-13