Receptors, circuits and neural dynamics for prediction

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While theoretical and computational frameworks for prediction exist, circuit and receptor-level mechanisms are unclear. Using high-density EEG and Bayesian modeling, we show that trial history and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual prediction task. Low-dose ketamine, a NMDA receptor blocker – but not the control drug dexmedetomidine – perturbed predictions, their representation in frontal cortex, and feedback to posterior cortex. This study suggests predictions depend on frontal alpha activity and NMDA receptors, and ketamine blocks access to learned predictive information.