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Corrigendum to: "Pilot study of on-treatment platelet reactivity at low shear stress and platelet activation status on aspirin or clopidogrel monotherapy in patients with TIA or ischaemic stroke" [JNS 2025 Vol 475, 123540].

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Related Experiment Video

Updated: Jun 12, 2026

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
08:39

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

Deformable mirror device spatial light modulators and their applicability to optical neural networks.

D R Collins, J B Sampsell, L J Hornbeck

    Applied Optics
    |June 18, 2010
    PubMed
    Summary

    Neural networks require modifiable interconnections, which current semiconductors struggle to provide. A deformable mirror device offers a novel solution for adaptable neural network hardware.

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    Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Materials Science

    Background:

    • Neural networks rely on numerous interconnections with modifiable weights.
    • Existing semiconductor technology faces limitations in creating adaptable interconnection hardware.
    • Efficiently modifying neural network weights is crucial for advanced AI applications.

    Purpose of the Study:

    • To address the challenge of modifiable interconnections in neural networks.
    • To introduce a novel hardware component for neural network architecture.
    • To explore the potential of spatial light modulators in AI hardware.

    Main Methods:

    • Investigating the properties of spatial light modulators.
    • Proposing the deformable mirror device (DMD) as a solution.
    • Analyzing the DMD's suitability for neural network interconnections.

    Main Results:

    • The deformable mirror device demonstrates potential for adaptable neural network interconnections.
    • This technology could overcome limitations of current semiconductor-based solutions.
    • The DMD offers a novel approach to implementing modifiable weights.

    Conclusions:

    • The deformable mirror device presents a promising breakthrough for neural network interconnectivity.
    • This innovation could significantly advance the development of adaptable AI hardware.
    • Further research into DMDs is warranted for practical neural network implementation.