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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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    Area of Science:

    • Computational Neuroscience
    • Machine Learning

    Background:

    • Traditional spiking learning requires precise temporal labels, which are often unavailable in real-world data.
    • Aggregate-label (AL) learning addresses this by using aggregated spike patterns but is computationally intensive and limited in deep networks.

    Purpose of the Study:

    • To develop a more computationally efficient and versatile spiking aggregate learning algorithm.
    • To improve the training of artificial neural networks using aggregated spike data.

    Main Methods:

    • Proposed an event-driven Spiking Aggregate Learning Algorithm (SALA).
    • Improved spike-threshold-surface (STS) calculation by analytically determining voltage peak values.
    • Extended the algorithm to multilayer networks using an event-driven strategy.

    Main Results:

    • The novel STS method significantly enhanced the efficiency of AL learning.
    • SALA demonstrated superior performance compared to conventional spiking algorithms in temporal clue recognition tasks.
    • Experiments covered temporal clue recognition, speech recognition, and neuromorphic image classification.

    Conclusions:

    • SALA offers a computationally efficient and effective approach for training spiking neural networks with aggregated labels.
    • The proposed method advances the application of spiking neural networks in complex real-world scenarios.