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Showing 1–18 of 18 results
Advanced filters: Author: James B. Aimone Clear advanced filters

  • Theilman and Aimone introduce a natively spiking algorithm for solving partial differential equations on large-scale neuromorphic computers and demonstrate the algorithm on Intel’s Loihi 2 neuromorphic research chip.

    • Bradley H. Theilman
    • James B. Aimone
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 1845-1857

  • The function of adult hippocampal neurogenesis is incompletely understood. Gage and colleagues describe the regulation and maturation of adult-born hippocampal neurons and review behavioural and computational modelling studies that indicate how adult-born neurons might contribute to hippocampus-dependent learning and memory.

    • Wei Deng
    • James B. Aimone
    • Fred H. Gage
    Reviews
    Nature Reviews Neuroscience
    Volume: 11, P: 339-350

  • A transistor made from atomically thin materials mimics the way in which connections between neurons are strengthened by activity. Two perspectives reveal why physicists and neuroscientists share equal enthusiasm for this feat of engineering.

    • Frank H. L. Koppens
    • James B. Aimone
    • Frances S. Chance
    News & Views
    Nature
    Volume: 624, P: 534-536

  • The current gap between computing algorithms and neuromorphic hardware to emulate brains is an outstanding bottleneck in developing neural computing technologies. Aimone and Parekh discuss the possibility of bridging this gap using theoretical computing frameworks from a neuroscience perspective.

    • James B. Aimone
    • Ojas Parekh
    Comments & OpinionOpen Access
    Nature Communications
    Volume: 14, P: 1-3

  • Today’s high-performance computing systems are nearing an ability to simulate the human brain at scale. This presents a new challenge: going forward, will the bigger challenge be the brain’s size or its complexity?

    • Felix Wang
    • James B. Aimone
    News & Views
    Nature Computational Science
    Volume: 4, P: 882-883

  • Karan P. Patel, Andrew Maicke and co-authors present a computational framework which tailors the hardware design for a specific requirement. Using their method, they demonstrate automatic codesign for the magnetic tunnel junction device simultaneously aiming at desirable performance and energy efficiency.

    • Karan P. Patel
    • Andrew Maicke
    • Catherine D. Schuman
    ResearchOpen Access
    Communications Engineering
    Volume: 4, P: 1-11

  • Dense calcium imaging combined with co-registered high-resolution electron microscopy reconstruction of the brain of the same mouse provide a functional connectomics map of tens of thousands of neurons of a region of the primary cortex and higher visual areas.

    • J. Alexander Bae
    • Mahaly Baptiste
    • Chi Zhang
    ResearchOpen Access
    Nature
    Volume: 640, P: 435-447

  • Neuromorphic processors promise to be a low-powered platform for deep learning, but require neural networks that are adapted for binary communication. The Whetstone method achieves this by gradually sharpening activation functions during the training process.

    • William Severa
    • Craig M. Vineyard
    • James B. Aimone
    Research
    Nature Machine Intelligence
    Volume: 1, P: 86-94

  • Immature dentate gyrus neurons are highly excitable and are thought to be more responsive to afferent activity than mature neurons. Here, the authors find stimulation of the entorhinal cortex paradoxically generates spiking in mature rather than immature neurons due to low synaptic connectivity of immature cells.

    • Cristina V. Dieni
    • Roberto Panichi
    • Linda Overstreet-Wadiche
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-13

  • Neuromorphic hardware designed to implement spiking neural networks for deep learning and artificial intelligence applications can also be used to solve non-cognitive computational tasks such as Monte Carlo methods.

    • J. Darby Smith
    • Aaron J. Hill
    • James B. Aimone
    Research
    Nature Electronics
    Volume: 5, P: 102-112

  • Approaches for the development of future at-scale neuromorphic systems based on principles of biointelligence are described, along with potential applications of scalable neuromorphic architectures and the challenges that need to be overcome.

    • Dhireesha Kudithipudi
    • Catherine Schuman
    • Steve Furber
    Reviews
    Nature
    Volume: 637, P: 801-812

  • A hybrid analogue–digital computing system based on memristive devices is capable of solving classic control problems with potentially a lower energy consumption and higher speed than fully digital systems.

    • Sam Green
    • James B. Aimone
    News & Views
    Nature Electronics
    Volume: 2, P: 96-97

  • The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.

    • Dhireesha Kudithipudi
    • Anurag Daram
    • Benjamin Epstein
    Reviews
    Nature Electronics
    Volume: 6, P: 807-822