A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks.
A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks.
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DOI：
10.3389/fnbot.2018.00035
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发表时间：
2018
影响因子：
3.1
通讯作者：
Knoll AC
中科院分区：
文献类型：
--
作者：
Bing Z;Meschede C;Röhrbein F;Huang K;Knoll AC
生物智能使用脉冲或尖峰处理信息，这使得这些生物能够在真实的世界中感知和行动，并且几乎在生活的各个方面都优于最先进的机器人。为了弥补这一不足，神经科学、电子学和计算机科学领域的新兴硬件技术和软件知识使得设计由尖峰神经网络（SNN）控制的生物逼真机器人成为可能，这些机器人受到大脑机制的启发。然而，基于SNN的机器人控制的全面审查仍然缺失。在本文中，我们回顾了过去十年在控制任务的脉冲神经网络领域的发展，特别关注快速新兴的机器人相关应用。我们首先强调了基于SNN的机器人任务在速度、能效和计算能力方面的主要推动力。然后，我们根据不同的学习规则对这些基于SNN的机器人应用进行分类，并将这些学习规则与相应的机器人应用进行详细说明。我们还简要介绍了一些现有的平台，这些平台提供了SNN和机器人模拟之间的交互，以进行探索和开发。最后，我们总结了我们的调查，预测未来的挑战和一些相关的潜在研究课题，在控制机器人的基础上SNN。
Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.
影响因子：
5.5
作者：
Adrian, ED
通讯作者：
Adrian, ED