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Showing 1–5 of 5 results
Advanced filters: Author: Alpha Renner Clear advanced filters

  • Neuromorphic computing has shown the capability of low-power real-time parallel computations, however, implementing the backpropagation algorithm entirely on a neuromorphic chip has remained challenging. The authors propose a spiking neural network implementation of the exact backpropagation algorithm fully on-chip without a computer in the loop.

    • Alpha Renner
    • Forrest Sheldon
    • Andrew Sornborger
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-14

  • Visual odometry, or self-motion estimation, is a fundamental task in robotics. Renner, Supic and colleagues introduce a neuromorphic algorithm for visual odometry that leverages hyperdimensional computing and hierarchical resonators. The approach estimates a robot’s motion from event-based vision, a step towards low-power machine vision for robotics.

    • Alpha Renner
    • Lazar Supic
    • Yulia Sandamirskaya
    Research
    Nature Machine Intelligence
    Volume: 6, P: 653-663

  • The inference procedure for analysing a visual scene presents a computational challenge. Renner, Supic and colleagues develop a neural network model, the hierarchical resonator, to determine the generative factors of variation of objects in simple scenes. The resonator was implemented on neuromorphic hardware, using a spike-timing code for complex numbers.

    • Alpha Renner
    • Lazar Supic
    • E. Paxon Frady
    Research
    Nature Machine Intelligence
    Volume: 6, P: 641-652

  • Brain-inspired neuromorphic algorithms and systems have shown essential advance in efficiency and capabilities of AI applications. In this Perspective, the authors introduce NeuroBench, a benchmark framework for neuromorphic approaches, collaboratively designed by researchers across industry and academia.

    • Jason Yik
    • Korneel Van den Berghe
    • Vijay Janapa Reddi
    ReviewsOpen Access
    Nature Communications
    Volume: 16, P: 1-24

  • Reduced glomerular filtration rate (eGFR) is a hallmark of chronic kidney disease. Here, Pattaro et al. conduct a meta-analysis to discover several new loci associated with variation in eGFR and find that genes associated with eGFR loci often encode proteins potentially related to kidney development.

    • Cristian Pattaro
    • Alexander Teumer
    • Caroline S. Fox
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-19