Arena Paolo, Fortuna Luigi, Frasca Mattia, Patané Luca
Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Università degli Studi di Catania, 95125 Catania, Italy.
IEEE Trans Neural Netw. 2009 Feb;20(2):202-16. doi: 10.1109/TNN.2008.2005134. Epub 2009 Jan 13.
In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.
在本文中，我们介绍了一个用于导航控制的脉冲神经元网络。研究了三个不同的例子，涉及复杂度不断增加的刺激。在第一个例子中，通过脉冲神经元网络在模拟机器人中实现避障。在第二个例子中，设计了第二层，旨在为机器人提供目标接近系统，使其能够朝着视觉目标移动。最后，引入了一个基于视觉线索的用于导航的脉冲神经元网络。在所有情况下，假设机器人依赖于对低级传感器的一些先验已知响应（即，在障碍物情况下对接触传感器的响应，在视觉目标情况下对接近目标传感器的响应，或在基于视觉线索导航时对视觉目标的响应）。基于这些知识，机器人必须学习对高级刺激（即，测距传感器或视觉输入）的响应。网络中包含了具有生物学合理性的脉冲时间依赖可塑性（STDP）范式，以使系统能够学习引导通过简单无结构环境的导航的高级响应。学习过程基于经典条件作用。
