Paolo Muratore

This paper develops an interpretable AI framework to map brain dynamics onto a latent space, identifying a cerebello-prefrontal signature associated with schizophrenia symptoms.

This study uses a deep CNN to analyze rat object vision performance and finds that rats exhibit more invariant perceptual strategies than CNNs, suggesting advanced visual processing capabilities.

The paper uses a mouse forelimb model to show that sensorimotor prediction errors are encoded in S1 cortex more than M1 during motor adaptation.

The paper shows that visual representations become more stable along rodent visual processing hierarchies, with intrinsic dynamics contributing to this stability.

The paper develops a mean-field formalism to predict the collective dynamics of large networks of complex spiking neurons, linking population models to electrophysiology.