Reviews: Long short-term memory and Learning-to-learn in networks of spiking neurons

Summary Recurrent networks of leaky integrate-and-fire neurons with (spike frequency) adaptation are trained with backpropagation-through-time (adapted to spiking neurons) to perform digit recognition (temporal MNIST), speech recognition (TIMIT), learning to learn simple regression tasks and learning to find a goal location in simple navigation tasks. The performances on temporal MNIST and TIMIT are similar to the one of LSTM-networks. The simple regression and navigation task demonstrate that connection weights exist that allow to solve simple tasks using the short-term memory of spiking neurons with adaptation, without the need of ongoing synaptic plasticity. Quality The selection of tasks is interesting, the results are convincing and the supplementary information seems to provide sufficient details to reproduce them. But the writing could be improved significantly.