Adriana Perez Rotondo

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.

The paper shows that deep-learning models of proprioception can be fooled by simulated muscle-tendon vibrations, producing illusions similar to those observed in humans.

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.

This paper uses mathematical analysis and simulations to show that cerebellar architecture, specifically size expansions, helps overcome challenges in online learning of dynamic tasks by enabling dense granule cell activity.

The paper shows that neural circuits have an optimal size for learning performance, where larger circuits can either enhance complex behaviors or improve learning of simpler tasks, but beyond a point, increased size impairs learning due to noise.