In this paper a new method called SCLA which stands for Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks. The environment consists of multiple squares of the same size and the robot only observes the neighboring squares of its current square. It should be stated that the robot only moves either up and down or right and left. The environment returns feedback to the learning automata to optimize its decision making in the next steps resulting in cellular automata training. Simultaneously a spiking neural network is trained to implement long term improvements and reductions on the paths. The results show that the integration of both cellular automata and spiking neural network ends up in reinforcing the proper paths and training time reduction at the same time.

翻译：本文提出了一种名为SCLA（Spiking based Cellular Learning Automata）的新方法，用于使移动机器人能够从任意随机初始位置到达目标。该方法是细胞自动机与脉冲神经网络相结合的产物。环境由多个相同尺寸的方格组成，机器人仅能观测其当前所在方格邻近的方格。需要说明的是，机器人仅能沿上下或左右方向移动。环境向学习自动机返回反馈信号，以优化其在后续步骤中的决策，从而实现细胞自动机的训练。与此同时，一个脉冲神经网络被训练用于对路径实施长期改进与缩减。结果表明，细胞自动机与脉冲神经网络的结合能够同时强化正确路径并缩短训练时间。