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Recurrent networks of spiking neurons (RSNNs) underlie the astoundingcomputing and learning capabilities of the brain. But computing and learningcapabilities of RSNN models have remained poor, at least in comparison withartificial neural networks (ANNs). We address two possible reasons for that.One is that RSNNs in the brain are not randomly connected or designed accordingto 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 arelargely unknown. But their functional contribution can be approximated throughpowerful optimization methods, such as backpropagation through time (BPTT).  A second major mismatch between RSNNs in the brain and models is that thelatter only show a small fraction of the dynamics of neurons and synapses inthe brain. We include neurons in our RSNN model that reproduce one prominentdynamical process of biological neurons that takes place at the behaviourallyrelevant time scale of seconds: neuronal adaptation. We denote these networksas LSNNs because of their Long short-term memory. The inclusion of adaptingneurons drastically increases the computing and learning capability of RSNNs ifthey are trained and configured by deep learning (BPTT combined with a rewiringalgorithm that optimizes the network architecture). In fact, the computationalperformance of these RSNNs approaches for the first time that of LSTM networks.In addition RSNNs with adapting neurons can acquire abstract knowledge fromprior learning in a Learning-to-Learn (L2L) scheme, and transfer that knowledgein order to learn new but related tasks from very few examples. We demonstratethis for supervised learning and reinforcement learning.

Long short-term memory and learning-to-learn in networks of spiking neurons