Biological neurons use spikes to process and learn temporally dynamic inputs in an energy and computationally efficient way. However, applying the state-of-the-art gradient-based supervised algorithms to spiking neural networks (SNN) is a challenge due to the non-differentiability of the activation function of spiking neurons. Employing surrogate gradients is one of the main solutions to overcome this challenge. Although SNNs naturally work in the temporal domain, recent studies have focused on developing SNNs to solve static image categorization tasks. In this paper, we employ a surrogate gradient descent learning algorithm to recognize twelve human hand gestures recorded by dynamic vision sensor (DVS) cameras. The proposed SNN could reach 97.2% recognition accuracy on test data.

中文翻译：

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使用监督尖峰神经网络的动作识别

生物神经元使用尖峰信号以能量和计算效率高的方式处理和学习时间动态输入。然而，由于尖峰神经元激活函数的不可微性，将最先进的基于梯度的监督算法应用于尖峰神经网络 (SNN) 是一项挑战。采用替代梯度是克服这一挑战的主要解决方案之一。尽管 SNN 自然地在时间域中工作，但最近的研究集中在开发 SNN 以解决静态图像分类任务。在本文中，我们采用替代梯度下降学习算法来识别由动态视觉传感器 (DVS) 相机记录的 12 种人体手势。所提出的 SNN 可以在测试数据上达到 97.2% 的识别准确率。