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Subtracting incoherent optical neuron model: analysis, experiment, and applications.

C H Wang, B K Jenkins

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    A novel incoherent optical neuron (ION) model enables optical neural networks to perform signal subtraction without phase sensitivity or photon-electron conversion. This advancement facilitates efficient optical computation for various neural network architectures.

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    Area of Science:

    • Optics
    • Neural Networks
    • Optical Computing

    Background:

    • Optical neural networks (ONNs) offer potential advantages over electronic systems.
    • Existing ONN models often face limitations such as phase sensitivity or reliance on photon-electron conversion for signal processing.

    Purpose of the Study:

    • To propose a new incoherent optical neuron (ION) model for ONNs.
    • To enable signal subtraction in optical systems without phase sensitivity or cumbersome photon-electron conversion.
    • To accommodate diverse neural network functionalities including positive/negative weights and excitatory/inhibitory inputs.

    Main Methods:

    • Utilizing two device responses to achieve signal subtraction.
    • Implementing conventional inner-product neuron units and Grossberg's mass action law neuron units.
    • Computer simulations to analyze nonlinearities, noise, and fan-in/fan-out capabilities.
    • Experimental demonstration using a liquid crystal light valve (LCCL) array.

    Main Results:

    • The ION model successfully performs signal subtraction, accommodating both excitatory and inhibitory inputs.
    • Demonstrated functional compatibility with various neuron unit types and network models.
    • Simulations provided insights into implementation considerations like noise and device nonlinearities.
    • Experimental validation confirmed optical excitation and inhibition in a 2D array.

    Conclusions:

    • The proposed ION model provides a viable approach for efficient optical neural network computation.
    • This model overcomes key limitations of coherent optical systems for neural network applications.
    • The technique supports flexible implementation of different neuron models and network architectures.