Proceedings of the Annual Meeting of the Cognitive Science Society

Artificial neural networks have been employed in many areas of cognitive systems research, ranging from low-levelcontrol tasks to high-level cognition. However, there is only little work on the use of spiking neural networks in these fields.In this project, we developed a virtual environment to explore solving navigation tasks using spiking neural networks. We firstused an existing experimental setup and compared the results to validate the developed environment. An evolutionary approachis used to set the parameters of a spiking neural network controlling a robot to navigate without collisions. In a second set ofexperiments, we trained the network via reinforcement learning which was implemented as a reward-based STDP protocol. Ourresults validate the correctness of the developed virtual environment and demonstrate the usefulness of using such a platform.The virtual environment guarantees the reproducibility of our experiments and can be easily adapted for future research.