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    Hebbian plasticity enhances spiking neural networks (SNNs) by enabling memory functions. This memory-augmented SNN architecture boosts generalization, learning, and cognitive capabilities for neuromorphic systems.

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    Area of Science:

    • Neuromorphic Engineering
    • Computational Neuroscience
    • Artificial Intelligence

    Background:

    • Spiking neural networks (SNNs) are crucial for energy-efficient neuromorphic hardware but lack biological memory capabilities.
    • Biological memory, essential for long-term information retention, is linked to Hebbian plasticity but its role in SNNs is underexplored.
    • Current artificial SNNs often struggle with generalization and complex cognitive tasks compared to biological systems.

    Purpose of the Study:

    • To propose Hebbian plasticity as fundamental for computations in both biological and artificial spiking neural systems.
    • To introduce a novel memory-augmented SNN architecture incorporating Hebbian synaptic plasticity.
    • To demonstrate the enhanced computational and learning capabilities of SNNs enriched with Hebbian plasticity.

    Main Methods:

    • Developed a novel SNN architecture integrating memory components.
    • Enriched the SNN architecture with Hebbian synaptic plasticity mechanisms.
    • Evaluated the performance of the memory-augmented SNNs on various cognitive tasks.

    Main Results:

    • The Hebbian-enriched SNNs demonstrated significant improvements in computational and learning abilities.
    • Enhanced performance was observed in out-of-distribution generalization, one-shot learning, and cross-modal association.
    • The architecture showed improved capabilities in language processing and reward-based learning tasks.

    Conclusions:

    • Hebbian synaptic plasticity is a fundamental principle for advancing SNN capabilities.
    • Memory-augmented SNNs with Hebbian plasticity offer enhanced versatility and cognitive functions.
    • This approach provides a pathway for building powerful cognitive neuromorphic systems.