A Biologically Inspired Unsupervised Artificial Visual System for Motion-Direction Detection via Hierarchical Agglomerative Clustering

Motion perception in biological vision is influenced by experience-dependent processes, during which direction-selective responses emerge and are further refined over time, although the underlying mechanisms remain partially understood. This study proposes a biologically inspired Hierarchical Agglomerative Clustering (HAC)-based unsupervised artificial visual system (AVS) for motion-direction detection. The model consists of a local motion-processing layer and a global motion inference layer. At the local level, motion-direction responses are extracted using retina-inspired local motion detection neurons (LMDN) to capture pixel-level spatiotemporal variation. These local direction responses are clustered using prototype-aware HAC to infer the global motion direction at the global level. Additionally, an unsupervised learning mechanism inspired by the concepts of neural plasticity and critical periods has been established. Extensive computer simulations are conducted under varying noise conditions and object scales. The results indicate that the proposed AVS maintained robust global motion-direction detection accuracy across diverse test conditions and exhibited biologically interpretable features resembling selected features of biological visual systems. In conclusion, this HAC-based unsupervised AVS provides a computational model for studying the formation of motion representations in visual systems and supports the development of artificial vision systems.