SPIKING GLOM: BIO-INSPIRED ARCHITECTURE FOR NEXT-GENERATION OBJECT RECOGNITION

Today, artificial neural networks (ANNs) have demonstrated extraordinary abilities in many cognition tasks. Nevertheless, the limitations of many ANN-based techniques are evident, such as the low energy efficiency and the lack of interpretability. To alleviate these problems, researchers have directed their attention to bio-inspired models, including energy-efficient Spiking Neural Networks (SNNs) and the GLOM model representing part-whole hierarchies in neural networks. In this paper, we propose a novel bio-inspired solution to next-generation object recognition. Specifically, we propose an energy-efficient and interpretable model -- Spiking GLOM by introducing spiking neurons and neuronal dynamics into the GLOM model. Moreover, we evaluate our model and its variants on CIFAR-10. Extensive experiments demonstrate the effectiveness of our proposed models for object recognition and show the superiority of our models in energy efficiency and interpretability.