Time series prediction and anomaly detection with recurrent spiking neural networks

In the recent years, Spiking Neural Networks have gain much attention from the research community. They can now be trained using the powerful gradient descent and have drifted from the neuroscience to the Machine Learning community. An abundant literature shows that they can perform well on classical Artificial Intelligence tasks such as image or signal classification while consuming less energy than state-of-the-art models like Convolutional Neural Networks. Yet, there is very little work about their performance on unsupervised anomaly detection and time-series prediction. Indeed, the processing of such temporal data requires different encoding and decoding mechanisms and rises questions about their capacity to model a dynamical signal with long term temporal dependencies. In this paper, we propose for the first time a Sparse Recurrent Spiking Neural Network with specific encoding and decoding mechanisms to successfully predict time-series and do Unsupervised Anomaly Detection. We also provide a framework to describe in detail our model computational costs and fairly compare them with state-of-the-art models. Despite improvable performances, we show that our model perform well on these tasks and open a door for further studies of such applications for Spiking Neural Networks.