Recurrent neural networks as versatile tools of neuroscience research

Recurrent neural networks (RNNs) are a class of computational models that are often used as a tool to explain neurobiological phenomena, considering anatomical, electrophysiological and computational constraints. RNNs can either be designed to implement a certain dynamical principle, or they can be trained by input–output examples. Recently, there has been large progress in utilizing trained RNNs both for computational tasks, and as explanations of neural phenomena. I will review how combining trained RNNs with reverse engineering can provide an alternative framework for modeling in neuroscience, potentially serving as a powerful hypothesis generation tool. Despite the recent progress and potential benefits, there are many fundamental gaps towards a theory of these networks. I will discuss these challenges and possible methods to attack them.