  Current neural decoding methods typically aim at explaining behavior based on
neural activity via supervised learning. However, since generally there is a
strong connection between learning of subjects and their expectations on
long-term rewards, we propose NeuRL, an inverse reinforcement learning approach
that (1) extracts an intrinsic reward function from collected trajectories of a
subject in closed form, (2) maps neural signals to this intrinsic reward to
account for long-term dependencies in the behavior and (3) predicts the
simulated behavior for unseen neural signals by extracting Q-values and the
corresponding Boltzmann policy based on the intrinsic reward values for these
unseen neural signals. We show that NeuRL leads to better generalization and
improved decoding performance compared to supervised approaches. We study the
behavior of rats in a response-preparation task and evaluate the performance of
NeuRL within simulated inhibition and per-trial behavior prediction. By
assigning clear functional roles to defined neuronal populations our approach
offers a new interpretation tool for complex neuronal data with testable
predictions. In per-trial behavior prediction, our approach furthermore
improves accuracy by up to 15% compared to traditional methods.
