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

• Published in: Pattern recognition letters 2024-08
• Main Authors: Ribeiro, Bernardete, Antunes, Francisco, Perdigão, Dylan, Silva, Catarina
• Format: Article
• Language: English
• Subjects: Convolutional neural networks; Hyperparameter optimization; Learning; Spiking Neural Networks
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2024.08.002

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. [Display omitted] •Effective SNN employing structured convolutional layers with LIF neurons.•Careful SNN hyperparameter optimization in a highly imbalanced dataset.•Development of a novel strategy for detecting online bank account fraud.•Comparison of neuromorphic-based approaches and gradient boosting machine learning.