Serotonin in the Bayesian brain

While theoretical models increasingly use Bayesian frameworks to explain neural processing, a significant gap exists in understanding how these mathematical principles are biologically implemented. This thesis proposes that serotonin serves as a biological mechanism for precision modulation in predictive processing, tested through experiments on exploratory behavior, perceptual illusions, and psychedelic-induced neural states.
The research reveals how serotonergic modulation in rodent whisking behavior demonstrates precision weighting as a key mechanism influencing sensory processing. Using active inference frameworks, the work shows serotonin modulates the precision of sensory inputs and prior habits, regulating exploratory behavior and environmental sampling.
Robotics experiments translate these biological insights into artificial systems, demonstrating how precision-based active inference can guide autonomous behavior in humanoid robots through adaptive sensorimotor control and efficient information seeking.
Theoretical work on attention and body ownership illusions explores precision control in embodied systems. Mathematical modeling of the rubber hand illusion shows how the brain arbitrates between competing sensory models via precision-weighted inference.
Analysis of psilocybin's neural effects demonstrates that this serotonergic agent increases chaotic brain responses and neural transition complexity, supporting theories that psychedelics decrease hierarchical neural communication precision through fundamental reorganization of neural dynamics.