Convolutional Spiking Neural Networks targeting learning and inference in highly imbalanced datasets

Abstract

Spiking Neural Networks (SNNs) are regarded as the next frontier in AI, as they can be implemented on neuromorphic hardware, paving the way for advancements in real-world applications in the field. SNNs provide a biologically inspired solution that is event-driven, energy-efficient and sparse. While showing promising results, there are challenges that need to be addressed. For example, the design-build-evaluate process for integrating the architecture, learning, hyperparameter optimization and inference need to be tailored to a specific problem. This is particularly important in critical high-stakes industries such as finance services. In this paper, we present SpikeConv, a novel deep Convolutional Spiking Neural Network (CSNN), and investigate this process in the context of a highly imbalanced online bank account opening fraud problem. Our approach is compared with Deep Spiking Neural Networks (DSNNs) and Gradient Boosting Decision Trees (GBDT) showing competitive results.