Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constrained by several challenges, prominently among them being performance optimization. In this study, we present a novel approach to enhance the performance of SNN for images through a new coding method that exploits bit plane representation. Our proposed technique is designed to improve the accuracy of SNN without increasing model size. Also, we investigate the impacts of color models of the proposed coding process. Through extensive experimental validation, we demonstrate the effectiveness of our coding strategy in achieving performance gain across multiple datasets. To the best of our knowledge, this is the first research that considers bit planes and color models in the context of SNN. By leveraging the unique characteristics of bit planes, we hope to unlock new potentials in SNNs performance, potentially paving the way for more efficient and effective SNNs models in future researches and applications.

翻译：脉冲神经网络（SNN）作为计算神经科学与人工智能领域一种颇具前景的范式，具有低能耗与小内存占用等优势。然而，其实际应用受到若干挑战的限制，其中性能优化尤为突出。本研究提出一种新颖方法，通过利用位平面表示的新型编码机制来提升SNN在图像任务上的性能。所提出的技术旨在不增加模型规模的前提下提高SNN的准确率。同时，我们探究了不同色彩模型对所提编码过程的影响。通过大量实验验证，我们证明了该编码策略在多个数据集上均能有效提升性能。据我们所知，这是首项在SNN框架中综合考虑位平面与色彩模型的研究。通过利用位平面的独特特性，我们期望能够释放SNN性能的新潜力，为未来研究与应用中更高效、更强大的SNN模型开辟道路。