IEEE Trans Neural Netw Learn Syst. 2022 Aug;33(8):3387-3399. doi: 10.1109/TNNLS.2021.3052804. Epub 2022 Aug 3.
Biological systems under a parallel and spike-based computation endow individuals with abilities to have prompt and reliable responses to different stimuli. Spiking neural networks (SNNs) have thus been developed to emulate their efficiency and to explore principles of spike-based processing. However, the design of a biologically plausible and efficient SNN for image classification still remains as a challenging task. Previous efforts can be generally clustered into two major categories in terms of coding schemes being employed: rate and temporal. The rate-based schemes suffer inefficiency, whereas the temporal-based ones typically end with a relatively poor performance in accuracy. It is intriguing and important to develop an SNN with both efficiency and efficacy being considered. In this article, we focus on the temporal-based approaches in a way to advance their accuracy performance by a great margin while keeping the efficiency on the other hand. A new temporal-based framework integrated with the multispike learning is developed for efficient recognition of visual patterns. Different approaches of encoding and learning under our framework are evaluated with the MNIST and Fashion-MNIST data sets. Experimental results demonstrate the efficient and effective performance of our temporal-based approaches across a variety of conditions, improving accuracies to higher levels that are even comparable to rate-based ones but importantly with a lighter network structure and far less number of spikes. This article attempts to extend the advanced multispike learning to the challenging task of image recognition and bring state of the arts in temporal-based approaches to a novel level. The experimental results could be potentially favorable to low-power and high-speed requirements in the field of artificial intelligence and contribute to attract more efforts toward brain-like computing.
生物系统的并行和基于尖峰的计算赋予了个体对不同刺激做出快速可靠反应的能力。因此，已经开发了尖峰神经网络（SNN）来模拟它们的效率，并探索基于尖峰的处理原理。然而，设计一个具有生物合理性和高效的用于图像分类的 SNN 仍然是一项具有挑战性的任务。以前的工作可以根据所采用的编码方案大致分为两类：速率和时间。基于速率的方案效率低下，而基于时间的方案通常在准确性方面的性能相对较差。开发一个既有效又高效的 SNN 是一个有趣且重要的问题。在本文中，我们重点关注基于时间的方法，通过大幅提高准确性来提高其性能，同时保持效率。我们开发了一个新的基于时间的框架，结合多尖峰学习，用于有效识别视觉模式。在我们的框架下，对不同的编码和学习方法进行了评估，使用了 MNIST 和 Fashion-MNIST 数据集。实验结果表明，我们的基于时间的方法在各种条件下都具有高效和有效的性能，将准确性提高到更高的水平，甚至可以与基于速率的方法相媲美，但重要的是，网络结构更轻，尖峰数量更少。本文试图将先进的多尖峰学习扩展到图像识别这一具有挑战性的任务，并将基于时间的方法的最新水平提升到一个新的水平。实验结果可能有利于人工智能领域对低功耗和高速的要求，并有助于吸引更多的研究人员关注类脑计算。
