论文标题
尖峰神经网络的最新进展和新的前沿
Recent Advances and New Frontiers in Spiking Neural Networks
论文作者
论文摘要
近年来，尖峰神经网络（SNN）由于其丰富的空间动力学，各种编码方法和事件驱动的特征而自然符合神经形态硬件，因此在脑启发的智能上受到了广泛的关注。随着SNN的发展，受脑启发的智力是一个受脑科学成就启发并针对人工通用智能的新兴研究领域，这变得越来越热。本文回顾了最新进展，并讨论了来自五个主要研究主题的SNN的新领域，包括基本要素（即尖峰神经元模型，编码方法和拓扑结构），神经形态数据集，优化算法，软件，软件和硬件框架。我们希望我们的调查能够帮助研究人员更好地了解SNN，并激发新作品以推进这一领域。
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.
