Embracing the Hairball: An Investigation of Recurrence in Spiking Neural Networks for Control

Recurrent, sparse spiking neural networks have been explored in contexts such as reservoir computing and winner-take-all networks. However, we believe there is the opportunity to leverage recurrence in spiking neural networks for other tasks, particularly for control. In this work, we show that evolved recurrent neural networks perform significantly better than feed-forward counterparts. We give two examples of the types of recurrent networks that are evolved and demonstrate that they are highly recurrent and unlike traditional, more structured recurrent neural networks that are used in deep learning literature.