  Training recurrent neural networks is predominantly achieved via
backpropagation through time (BPTT). However, this algorithm is not an optimal
solution from both a biological and computational perspective. A more efficient
and biologically plausible alternative for BPTT is e-prop. We investigate the
applicability of e-prop to long short-term memorys (LSTMs), for both supervised
and reinforcement learning (RL) tasks. We show that e-prop is a suitable
optimization algorithm for LSTMs by comparing it to BPTT on two benchmarks for
supervised learning. This proves that e-prop can achieve learning even for
problems with long sequences of several hundred timesteps. We introduce
extensions that improve the performance of e-prop, which can partially be
applied to other network architectures. With the help of these extensions we
show that, under certain conditions, e-prop can outperform BPTT for one of the
two benchmarks for supervised learning. Finally, we deliver a proof of concept
for the integration of e-prop to RL in the domain of deep recurrent Q-learning.
