Before revision:  We found that the resulting RSNNs approximate, for the first time , the computational power of LSTM networks on two common benchmark tasks. Furthermore, our results show that recent successes with applications of Learning-to-Learn (L2L)to LSTM networks can be ported to RSNNs. This opens the door to the investigation of L2L in data-based models for neural networks of the brain, whose activity can -- unlike that of LSTM networks -- be compared directly with recordings from neurons in the brain.
After revision:  We found that the first time that of LSTM networks -- be compared directly with recordings from neurons in the brain.