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    This summary is machine-generated.

    We developed a novel all-optical spiking neural network (SNN) using vertical-cavity surface-emitting lasers (VCSELs) for supervised learning. This photonic neuromorphic system efficiently recognizes numbers without lateral inhibition, paving the way for low-power computing.

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    Area of Science:

    • Neuromorphic Engineering
    • Photonics
    • Artificial Intelligence

    Background:

    • Spiking neural networks (SNNs) mimic biological brains for efficient computation.
    • Vertical-cavity surface-emitting lasers (VCSELs) offer potential for compact, low-power optical computing.

    Purpose of the Study:

    • To propose a computing primitive for an all-optical SNN using VCSELs for supervised learning.
    • To develop a unified neuron-synapse model for all-optical SNNs based on VCSEL dynamics.
    • To demonstrate the feasibility of optical character recognition using the proposed SNN.

    Main Methods:

    • Established a spike-timing-dependent plasticity (STDP) model based on vertical-cavity semiconductor optical amplifier (VCSOA) dynamics.
    • Developed a self-consistent unified model for an all-optical SNN.
    • Trained and tested the SNN for optical character recognition of numbers using spatiotemporal patterns.

    Main Results:

    • The all-optical SNN successfully recognized ten numbers via supervised learning.
    • The system demonstrated neuron-like dynamics and STDP function.
    • Lateral inhibition was not required, simplifying hardware implementation.

    Conclusions:

    • The proposed VCSEL-based all-optical SNN computing primitive is a novel approach for supervised learning.
    • This work enables architecture-algorithm codesigns for photonic neuromorphic systems.
    • The findings pave the way for large-scale, low-power, fully VCSEL-based neuromorphic systems.