  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.
