Computational models are increasingly used to relate mouse brain structure, neural activity and behavior, but most models still learn from task data with limited constraints from biological circuit organization. Here we present DigiMus, a connectome-informed spiking framework for multi-region-capable mouse neural-behavior modeling. DigiMus combines leaky integrate-and-fire spiking dynamics with brain-region-specific motif regularization in a trainable sequence-modeling architecture, allowing directed three-node circuit motifs derived from 38,481 reconstructed neuronal morphologies across approximately 50 brain regions to guide recurrent coupling during learning. We evaluate DigiMus on 18 rule-based cognitive tasks spanning sensorimotor mapping and perceptual decision-making, and on three mouse neural decoding datasets involving auditory discrimination, fixed-interval licking and visual decoding. Across synthetic tasks, DigiMus showed stable performance relative to TCN, LSTM and Transformer baselines, with stronger advantages in more complex decision-making settings. In real neural datasets, single-region instantiations of DigiMus produced small, consistent and dataset-dependent improvements over a structure-free sequence baseline, while retaining motif-prior signatures in trained connectivity. Internal state analyses further linked task-dependent state dynamics to behavioral error patterns. These results suggest that connectome-derived structural priors can shape neural sequence models, and establish DigiMus as a modular, connectome-informed workflow for mouse neural-behavior modeling and hypothesis generation, rather than a complete digital reconstruction.
Liu, Y., Zhang, X., Chen, X., Hao, C., Yao, W., Zhang, J., Sun, Y., Zhang, T.
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