BioMotion-SNN: Spiking neural network modeling for visual motion processing

Authors: Ying Liu, Jiajun Mei, Tingting Feng, Tao Zhang, Hong Qu, Yi Zhang
Published: 2025-10-01
Journal: Neural Networks, Vol 195, Article 108239
DOI: 10.1016/j.neunet.2025.108239

Abstract

Neuroscience remains to be challenged by the decoding of the neural processes underlying biological motion perception. To address this, we propose BioMotion-SNN, a spiking neural network (SNN) framework inspired by the MT-MST pathways, designed to model the dynamic interactions between these brain regions. Grounded in biological experimental phenomena, BioMotion-SNN processes event-driven stimuli in a manner closely resembling real sensory inputs, setting it apart from conventional models reliant on static datasets and predefined labels. The framework incorporates contrastive self-supervised learning with a motion-perception contrastive loss function to enhance feature representation, while L1-norm-based synaptic pruning mimics sparse biological connectivity by reducing redundant connections. Leveraging real electrophysiological data augmented through controlled transformations, BioMotion-SNN reduces the need for extensive biological data collection, enriches dataset diversity, and bridges the gap between experimental neuroscience and computational modeling. Achieving a classification accuracy of 93.00 %, the framework effectively captures complex motion patterns and establishes a novel paradigm for integrating computational modeling with empirical neuroscience. Our data/codes are available at https://github.com/BrainCogLab/biomotion_snn.