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<p>Spiking Neural Networks (SNNs) are a breakthrough in artificial intelligence (AI), inspired by the event-driven and temporal dynamics of biological brain systems. SNNs, as opposed to conventional artificial neural networks, use discrete spikes to process information, allowing for energy-efficient computing and the native handling of sequential and temporal data. With a focus on physiologically inspired mechanisms like Spike-Timing Dependent Plasticity (STDP), this study introduces the basic ideas of SNNs, including their neuron models, spike encoding strategies, and learning algorithms. We investigate the various uses of SNNs in fields like real-time sensory processing, neuromorphic computing, robotics, and spatiotemporal pattern recognition. The study also emphasizes how SNNs and neuromorphic hardware work together to provide low-power, high-performance computing by utilizing SNNs’ event-driven architecture. Notwithstanding their potential, SNNs have scalability and training issues, which we address along with new approaches and possible lines of inquiry. SNNs can potentially improve the effectiveness and capability of intelligent systems in dynamic, real-world situations by bridging the gap between AI and neuroscience.</p>

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Spiking neural networks: a comprehensive review of diverse applications, research progress, challenges and future research directions

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Parijata Majumdar

摘要

Spiking Neural Networks (SNNs) are a breakthrough in artificial intelligence (AI), inspired by the event-driven and temporal dynamics of biological brain systems. SNNs, as opposed to conventional artificial neural networks, use discrete spikes to process information, allowing for energy-efficient computing and the native handling of sequential and temporal data. With a focus on physiologically inspired mechanisms like Spike-Timing Dependent Plasticity (STDP), this study introduces the basic ideas of SNNs, including their neuron models, spike encoding strategies, and learning algorithms. We investigate the various uses of SNNs in fields like real-time sensory processing, neuromorphic computing, robotics, and spatiotemporal pattern recognition. The study also emphasizes how SNNs and neuromorphic hardware work together to provide low-power, high-performance computing by utilizing SNNs’ event-driven architecture. Notwithstanding their potential, SNNs have scalability and training issues, which we address along with new approaches and possible lines of inquiry. SNNs can potentially improve the effectiveness and capability of intelligent systems in dynamic, real-world situations by bridging the gap between AI and neuroscience.