Alessandro Marin Vargas

This paper develops a closed-loop muscle-level model of primate grasping using imitation learning and neural recordings, revealing muscle-centric and latent-goal codes in sensorimotor cortex.

This paper develops a physics-informed neural network model to simulate muscle spindle function, validated across species and datasets, revealing key drivers of variability in proprioceptive responses.

The paper uses task-driven neural networks to predict neural activity in proprioceptive pathways during reaching tasks, finding that task-optimized models outperform other models.