Before revision:  In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through learning-to-learn (L2L ) methods from machine learning , that have so far been applied primarily to LSTM networks and apparently never to SSNs. This preliminary report on arXiv will be replaced by a more detailed version in about a month .
After revision:  In addition, the computing and learning capabilities of LSNNs can be substantially enhanced through 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. Furthermore, meta-reinforcement learning (meta-RL) shows that LSNNs can learn and execute complex exploration and exploitation strategies .