Zheng Yu, Xue Jingfeng, Liu Jing, Zhang Yanjun
Beijing Institute of Technology, Beijing 100081, China.
Biomimetics (Basel). 2025 Jan 14;10(1):48. doi: 10.3390/biomimetics10010048.
A future unmanned system needs the ability to perceive, decide and control in an open dynamic environment. In order to fulfill this requirement, it needs to construct a method with a universal environmental perception ability. Moreover, this perceptual process needs to be interpretable and understandable, so that future interactions between unmanned systems and humans can be unimpeded. However, current mainstream DNN (deep learning neural network)-based AI (artificial intelligence) is a 'black box'. We cannot interpret or understand how the decision is made by these AIs. An SNN (spiking neural network), which is more similar to a biological brain than a DNN, has the potential to implement interpretable or understandable AI. In this work, we propose a neuron group-based structural learning method for an SNN to better capture the spatial and temporal information from the external environment, and propose a time-slicing scheme to better interpret the spatial and temporal information of responses generated by an SNN. Results show that our method indeed helps to enhance the environment perception ability of the SNN, and possesses a certain degree of robustness, enhancing the potential to build an interpretable or understandable AI in the future.
未来的无人系统需要在开放动态环境中具备感知、决策和控制能力。为满足这一要求，需要构建一种具有通用环境感知能力的方法。此外，这种感知过程需要具有可解释性和可理解性，以便未来无人系统与人类之间的交互能够畅通无阻。然而，当前基于深度神经网络（DNN）的主流人工智能（AI）是一个“黑匣子”。我们无法解释或理解这些人工智能是如何做出决策的。与DNN相比，更类似于生物大脑的脉冲神经网络（SNN）有潜力实现可解释或可理解的人工智能。在这项工作中，我们为SNN提出了一种基于神经元组的结构学习方法，以更好地从外部环境中捕获时空信息，并提出了一种时间切片方案，以更好地解释SNN产生的响应的时空信息。结果表明，我们的方法确实有助于增强SNN的环境感知能力，并具有一定程度的鲁棒性，增强了未来构建可解释或可理解人工智能的潜力。
