Liu Siying, Leung Vincent C H, Dragotti Pier Luigi
Communications and Signal Processing Group, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom.
Front Neurosci. 2023 Oct 2;17:1266003. doi: 10.3389/fnins.2023.1266003. eCollection 2023.
Spiking neural networks (SNNs) are well-suited to process asynchronous event-based data. Most of the existing SNNs use rate-coding schemes that focus on firing rate (FR), and so they generally ignore the spike timing in events. On the contrary, methods based on temporal coding, particularly time-to-first-spike (TTFS) coding, can be accurate and efficient but they are difficult to train. Currently, there is limited research on applying TTFS coding to real events, since traditional TTFS-based methods impose one-spike constraint, which is not realistic for event-based data. In this study, we present a novel decision-making strategy based on first-spike (FS) coding that encodes FS timings of the output neurons to investigate the role of the first-spike timing in classifying real-world event sequences with complex temporal structures. To achieve FS coding, we propose a novel surrogate gradient learning method for discrete spike trains. In the forward pass, output spikes are encoded into discrete times to generate FS times. In the backpropagation, we develop an error assignment method that propagates error from FS times to spikes through a Gaussian window, and then supervised learning for spikes is implemented through a surrogate gradient approach. Additional strategies are introduced to facilitate the training of FS timings, such as adding empty sequences and employing different parameters for different layers. We make a comprehensive comparison between FS and FR coding in the experiments. Our results show that FS coding achieves comparable accuracy to FR coding while leading to superior energy efficiency and distinct neuronal dynamics on data sequences with very rich temporal structures. Additionally, a longer time delay in the first spike leads to higher accuracy, indicating important information is encoded in the timing of the first spike.
脉冲神经网络（SNN）非常适合处理基于异步事件的数据。现有的大多数SNN使用专注于发放率（FR）的速率编码方案，因此它们通常忽略事件中的脉冲时间。相反，基于时间编码的方法，特别是首次脉冲时间（TTFS）编码，可能准确且高效，但难以训练。目前，将TTFS编码应用于实际事件的研究有限，因为传统的基于TTFS的方法施加了单脉冲约束，这对于基于事件的数据来说并不现实。在本研究中，我们提出了一种基于首次脉冲（FS）编码的新型决策策略，该策略对输出神经元的FS时间进行编码，以研究首次脉冲时间在对具有复杂时间结构的现实世界事件序列进行分类中的作用。为了实现FS编码，我们提出了一种用于离散脉冲序列的新型替代梯度学习方法。在前向传播中，输出脉冲被编码为离散时间以生成FS时间。在反向传播中，我们开发了一种误差分配方法，该方法通过高斯窗口将误差从FS时间传播到脉冲，然后通过替代梯度方法对脉冲进行监督学习。还引入了其他策略来促进FS时间的训练，例如添加空序列和对不同层采用不同参数。我们在实验中对FS和FR编码进行了全面比较。我们的结果表明，FS编码在具有非常丰富时间结构的数据序列上实现了与FR编码相当的准确性，同时具有更高的能量效率和独特的神经元动力学。此外，首次脉冲中较长的时间延迟导致更高的准确性，表明重要信息编码在首次脉冲的时间中。
