论文标题
尖峰神经网络模拟动眼系统的结构，不需要学习才能控制仿生机器人头部
A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head
论文作者
论文摘要
机器人视觉引入了在不断变化的环境中实时处理快速变化的嘈杂信息的要求。在现实环境中，很难生存的方便假设，例如静态摄像头系统和深度学习算法，吞噬了大量理想的略有变化的数据。利用有关眼动的神经连接组的最新研究，我们设计了一个神经形态的眼动控制器，并将其放置在我们内部仿生机器人头部原型的核心。控制器是独一无二的，因为（1）所有数据均由尖峰神经网络（SNN）编码和处理，并且（2）通过模仿相关的大脑区域的拓扑，SNN在生物学上可以解释，并且不需要培训才能运行。在这里，我们报告了机器人的目标跟踪能力，证明其眼睛运动学类似于人类眼睛研究中报道的动力学，并表明可以使用一种生物学约束的学习，尽管不是SNN功能，但可以用于进一步完善其性能。这项工作与我们持续开发节能神经形态SNN的努力相吻合，并利用其新兴智力以用多功能性和鲁棒性来控制仿生机器人。
Robotic vision introduces requirements for real-time processing of fast-varying, noisy information in a continuously changing environment. In a real-world environment, convenient assumptions, such as static camera systems and deep learning algorithms devouring high volumes of ideally slightly-varying data are hard to survive. Leveraging on recent studies on the neural connectome associated with eye movements, we designed a neuromorphic oculomotor controller and placed it at the heart of our in-house biomimetic robotic head prototype. The controller is unique in the sense that (1) all data are encoded and processed by a spiking neural network (SNN), and (2) by mimicking the associated brain areas' topology, the SNN is biologically interpretable and requires no training to operate. Here, we report the robot's target tracking ability, demonstrate that its eye kinematics are similar to those reported in human eye studies and show that a biologically-constrained learning, although not required for the SNN's function, can be used to further refine its performance. This work aligns with our ongoing effort to develop energy-efficient neuromorphic SNNs and harness their emerging intelligence to control biomimetic robots with versatility and robustness.
