Understanding the neural mechanisms underlying biological motion perception remains a significant challenge in neuroscience. To further explore this mechanism, we construct the BioMotion-SNN model using real bioneural data from the MT to MST regions in macaques. To characterize neuron activity within specific time windows, we propose the windows learning strategy, which employs windowed learning to extract crucial information related to specific events or stimuli. By analyzing the connectivity structure of the BioMotion-SNN model, we identify regular projection patterns from MT to MST, reflected in the varying response characteristics of MT neurons based on their projection strength to different MST neuron populations. This work not only verifies some biological properties of MST neurons, but also underscores the potential of neurodevelopmental SNN models, driven byreal electrophysiological data, in improving our understanding of neural processing.