Recent advances in event-based neuromorphic systems have resulted in significant interest in the use and development of spiking neural networks (SNNs). However, the non-differentiable nature of spiking neurons makes SNNs incompatible with conventional backpropagation techniques. In spite of the significant progress made in training conventional deep neural networks (DNNs), training methods for SNNs still remain relatively poorly understood. In this paper, we present a novel framework for training recurrent SNNs. Analogous to the benefits presented by recurrent neural networks (RNNs) in learning time series models within DNNs, we develop SNNs based on long short-term memory (LSTM) networks. We show that LSTM spiking networks learn the timing of the spikes and temporal dependencies. We also develop a methodology for error backpropagation within LSTM-based SNNs. The developed architecture and method for backpropagation within LSTM-based SNNs enable them to learn long-term dependencies with comparable results to conventional LSTMs.

中文翻译：

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长短期记忆尖峰网络及其应用

基于事件的神经形态系统的最新进展引起了人们对尖峰神经网络 (SNN) 的使用和开发的极大兴趣。然而，尖峰神经元的不可微性使 SNN 与传统的反向传播技术不兼容。尽管在训练传统深度神经网络 (DNN) 方面取得了重大进展，但对 SNN 的训练方法仍然知之甚少。在本文中，我们提出了一种新的训练循环 SNN 的框架。类似于循环神经网络 (RNN) 在学习 DNN 中的时间序列模型方面的优势，我们开发了基于长短期记忆 (LSTM) 网络的 SNN。我们展示了 LSTM 尖峰网络学习尖峰的时间和时间依赖性。我们还开发了一种在基于 LSTM 的 SNN 中进行错误反向传播的方法。在基于 LSTM 的 SNN 中开发的反向传播架构和方法使他们能够学习长期依赖关系，结果与传统 LSTM 相当。