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SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations.

Luca Manneschi, Andrew C Lin, Eleni Vasilaki

    IEEE Transactions on Neural Networks and Learning Systems
    |August 16, 2021
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    Summary
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    We introduce SpaRCe, a novel method for creating sparse neural networks by using learnable neuron thresholds. This approach enhances performance and combats catastrophic forgetting in reservoir computing models.

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    Area of Science:

    • Computational Neuroscience
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Sparse neural networks are prevalent in both biological brains and artificial systems.
    • Sparsity in machine learning often involves weight penalties, while in neuroscience it relates to high spiking thresholds.
    • Existing reservoir computing models can suffer from issues like catastrophic forgetting.

    Purpose of the Study:

    • To introduce a new method, SpaRCe, for inducing sparsity in reservoir computing networks.
    • To optimize sparsity levels without altering the core reservoir dynamics.
    • To improve classification performance and mitigate catastrophic forgetting.

    Main Methods:

    • Implemented neuron-specific learnable activity thresholds in a reservoir computing network.
    • Utilized an online gradient descent rule to learn both read-out weights and thresholds.
    • Balanced threshold learning by reducing interneuronal correlations and increasing task-relevant neuron activity.

    Main Results:

    • SpaRCe demonstrated improved performance on classification tasks compared to standard reservoir computing.
    • The method effectively optimized sparsity levels within the network.
    • SpaRCe alleviated catastrophic forgetting by promoting task-specialized neurons.

    Conclusions:

    • SpaRCe offers an effective way to introduce and control sparsity in reservoir computing.
    • Learnable thresholds enhance model performance and robustness against catastrophic forgetting.
    • This approach bridges concepts from machine learning sparsity and biological neural activity thresholds.