Spiking Neural Networks for Predictive and Explainable Modelling of Multimodal Streaming Data with a Case Study on Financial Time-series and Online News

Abstract

AbstractHuman intelligence is characterized by the ability to incrementally integrate different sources of information for a better decision making. This paper argues that brain-inspired spiking neural networks (SNN) can be used for predictive and explainable modelling of multimodal streaming data. The paper proposes a new method, based on the brain-inspired SNN architecture NeuCube, where, first, all streaming data are represented as numerical times series in the same time domain. Then a NeuCube model is incrementally trained on the integrated time series and continuously interpreted. The method is illustrated on integrated modelling of financial time series and online news. In contrast to traditional machine learning techniques, the proposed method reveals the dynamic interaction between all types of temporal variables and their impact on the model accuracy. The method is applicable on a wide range of multimodal time series, such as financial, medical, environmental, supporting also the use of massively parallel and low energy neuromorphic hardware.