Learning Spiking Neural Controllers for In-silico Navigation Experiments

Artificial neural networks have been employed in many areas of cognitive systems research, ranging from low-level control tasks to high-level cognition. However, there is only few work on the use of spiking neural networks in these fields. Unlike ar- tificial neurons, spiking neuron models are designed to approx- imate the dynamics of biological neurons. In this work, we developed a virtual environment to explore solving navigation tasks using spiking neural networks. We first used an exper- imental setup inspired by Floreano and Mattiussi (2001) and compared the results to validate the developed environment. An evolutionary approach is used to set the parameters of a spiking neural network controlling a robot to navigate without collisions. In a second set of experiments, we trained the net- work via reinforcement learning which was implemented as a reward-based STDP protocol. Our results validate the correct- ness 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.     «

Artificial neural networks have been employed in many areas of cognitive systems research, ranging from low-level control tasks to high-level cognition. However, there is only few work on the use of spiking neural networks in these fields. Unlike ar- tificial neurons, spiking neuron models are designed to approx- imate the dynamics of biological neurons. In this work, we developed a virtual environment to explore solving navigation tasks using spiking neural networks. We first used an exp...     »