Before revision:  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. In particular, L2L shows that RSNNs can learn large families of non-linear transformations from very few examples , using previously unknown network learning mechanisms.
After revision:  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 . In addition RSNNs with adapting neurons can acquire abstract knowledge from prior learning in a Learning-to-Learn ( L2L shows that RSNNs can learn large families of non-linear transformations from very few examples , using previously unknown network learning mechanisms.