Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics

The thalamus plays a crucial role in motor control but its complex circuitry remains poorly understood. Existing computational models often lack anatomical detail, hindering investigations on how structure influences function. Here we present a data-driven 3D anatomical scaffold model (a geometrically constrained virtual circuit) of mouse motor thalamic nuclei, built by integrating publicly available datasets, anatomical descriptions, geometric constraints and circuit-level findings to reproduce topographical organization and structural boundaries observed experimentally. Network simulations demonstrate sustained spindle oscillations at physiologically realistic frequencies, with propagation velocities matching observations from thalamic slices. Systematic ablation reveals that both topography and distance-dependent synaptic weights are necessary for physiological dynamics, establishing architectural design principles for spatially organized circuits. These findings generalize beyond the motor thalamus: the open-source pipeline provides a reusable framework for data-driven circuit reconstruction, linking anatomical organisation to emergent network dynamics across brain regions. Data-driven 3D modeling of mouse motor thalamic nuclei reveals that topography and spatial weight scaling are necessary for physiological spindle oscillations, uncovering architectural design principles of spatially organized thalamic circuits.