Before revision:  But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons, such as Long Short-Term Memory (LSTM) networks. In this article, we investigate whether deep learning can improve RSNN performance. 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:  But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with ANNs. We address two possible reasons for that. One is that RSNNs in the brain are not randomly connected or designed according to simple rules, and they do not start learning as a tabula rasa network. Rather, RSNNs in the brain were optimized for their tasks through evolution, development, and prior experience. Details of these optimization processes are largely unknown. But their functional contribution can be approximated through powerful optimization methods, such as 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 .