Spiking neural networks for video analysis: An in-depth review of models and architectures

Deep Learning (DL) has revolutionized various industries, continuously evolving with new architectures and concepts. Among these, Spiking Neural Networks (SNNs) have emerged as a promising, energy-efficient, and biologically inspired computing paradigm, particularly for video analysis tasks that require spatial and temporal feature processing. With their event-driven design and intrinsic temporal capabilities, SNNs hold significant potential for real-time, energy-efficient systems. This paper comprehensively reviews state-of-the-art SNN advancements in video analysis, focusing on models, architectures, training strategies, and optimization techniques for spatio-temporal data processing. It also highlights benchmarks, evaluation metrics, and emerging trends, offering a roadmap for future research. To demonstrate the effectiveness of SNNs, we introduce SpikeActNet, an action recognition model, and compare its performance against conventional CNN models like C3D, I3D, and ResNet. Experimental results validate SNNs' robustness and generalization capabilities, positioning them as a promising alternative for next-generation video analytics. Our findings are a valuable foundation for future research and advancements in SNN-based video analysis.