Before revision:  We applied backpropagation through time (BPTT) , augmented by biologically inspired heuristics for synaptic rewiring, to RSNNs whose inherent time constants were enriched through simple models for adapting spiking neurons .
After revision:  We applied backpropagation through time (BPTT) . A second major mismatch between RSNNs in the brain and models is that the latter only show a small fraction of the dynamics of neurons and synapses in the brain. We include neurons in our RSNN model that reproduce one prominent dynamical process of biological neurons that takes place at the behaviourally relevant time scale of seconds: neuronal adaptation. We denote these networks as LSNNs because of their Long short-term memory. The inclusion of adapting neurons drastically increases the computing and learning capability of RSNNs if they are trained and configured by deep learning (BPTT combined with a rewiring algorithm that optimizes the network architecture) .