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

Authors: Francesco Jamal Sheiban, Alberto Antonietti, Muhammed Furkan Beyazyüz, Robin De Schepper, Carmen Alonso-Martínez, Mario Rubio-Teves, Francisco Clascá, Egidio D’Angelo and Alessandra Pedrocchi

Keywords

Neuroengineering, Thalamus, Computational Neuroscience, Brain Simulation

Summary

The thalamus plays a crucial role in motor control, but its complex circuits remain poorly understood. Existing computational models often lack anatomical detail, hindering investigations into how structure influences function. Here we present a data-driven 3D anatomical model (a virtual circuit with geometric constraints) of the motor thalamic nuclei in the mouse, constructed by integrating public datasets, anatomical descriptions, geometric constraints and circuit-level results to reproduce the topographical organisation and structural boundaries observed experimentally. Network simulations show spindle oscillations at physiologically realistic frequencies, with propagation speeds that correspond to observations made on thalamic sections. Systematic ablation reveals that both the topography and distance-dependent synaptic weights are necessary for physiological dynamics, establishing architectural design principles for spatially organised circuits. These findings generalise 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.

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