Liu Junxiu, Hua Yifan, Yang Rixing, Luo Yuling, Lu Hao, Wang Yanhu, Yang Su, Ding Xuemei
School of Electronic Engineering, Guangxi Normal University, Guilin, China.
College of Innovation and Entrepreneurship, Guangxi Normal University, Guilin, China.
Front Neurosci. 2022 Jun 30;16:905596. doi: 10.3389/fnins.2022.905596. eCollection 2022.
Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.
脉冲神经网络（SNNs）因其高信息处理能力以及对生物神经网络行为的精确模拟，常被视为第三代人工神经网络（ANNs）。尽管近年来对SNNs的研究相当活跃，但将SNNs应用于各种潜在应用仍存在一些挑战，尤其是在机器人控制方面。在本研究中，提出了一种基于奖励调制的脉冲时间依赖可塑性的受生物启发的自主学习算法，其中使用了一种新颖的奖励生成机制来为学习和决策过程生成奖励信号。通过移动机器人避障任务对所提出的学习算法进行了评估，实验结果表明，采用该算法的移动机器人具有良好的学习能力。经过一些学习试验后，机器人能够成功避开环境中的障碍物。这为在典型机器人任务场景中设计和应用具有自主学习能力的受生物启发机器人提供了一种替代方法。
