Recurrent networks of spiking neurons (RSNNs) underlie the astounding
computing and learning capabilities of the brain. But computing and learning
capabilities of RSNN models have remained poor, at least in comparison with
artificial neural networks (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).
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). In fact, the computational
performance of these RSNNs approaches for the first time that of LSTM networks.
In addition RSNNs with adapting neurons can acquire abstract knowledge from
prior learning in a Learning-to-Learn (L2L) scheme, and transfer that knowledge
in order to learn new but related tasks from very few examples. We demonstrate
this for supervised learning and reinforcement learning.

研究了如何通过深度学习将适应性神经元包含在递归脉冲神经元网络（RSNNs）中，从而优化网络结构，实现知识迁移和在少数示例中进行学习、手段是 BPTT 算法。