School of Psychology, University of Ottawa, Ottawa, Ontario, Canada.
Department of Computer Science, Cégep du Vieux Montréal, Montréal, Quebec, Canada.
Comput Intell Neurosci. 2019 Apr 1;2019:8361369. doi: 10.1155/2019/8361369. eCollection 2019.
This paper proposes an artificial spiking neural network (SNN) sustaining the cognitive abstract process of spatial concept learning, embedded in virtual and real robots. Based on an operant conditioning procedure, the robots learn the relationship of horizontal/vertical and left/right visual stimuli, regardless of their specific pattern composition or their location on the images. Tests with novel patterns and locations were successfully completed after the acquisition learning phase. Results show that the SNN can adapt its behavior in real time when the rewarding rule changes.
本文提出了一种人工尖峰神经网络（SNN），该网络嵌入虚拟和真实机器人中，支持空间概念学习的认知抽象过程。基于操作性条件作用过程，机器人学习水平/垂直和左/右视觉刺激之间的关系，而不考虑其特定的图案组成或在图像上的位置。在获得学习阶段之后，成功完成了针对新图案和位置的测试。结果表明，当奖励规则发生变化时，SNN 可以实时调整其行为。
