Ahn, J.; Kim, S.; Park, J.; Woo, S.; Sohn, H.; Lee, J.

Prior expectations guide sensorimotor behavior when sensory information is uncertain, yet the cellular mechanisms underlying this integration remain elusive. Here, we investigate how priors in sensory motion direction shape neural population dynamics by combining recurrent neural network (RNN) modeling with macaque smooth pursuit behavior and middle temporal area (area MT) electrophysiology. Our RNN model reveals that prior expectations are implemented by elevating firing thresholds in neurons tuned away from the expected direction. This selective inhibition sharpens population tuning and reduces behavioral variability under weak sensory information conditions. We validated this prediction in vivo: delta-band local field potentials in area MT, where the phase reflects neural excitability states, exhibited direction-specific phase shifts that scaled with the deviation from the expected direction. These findings demonstrate that prior expectations enhance behavioral reliability through tuned inhibitory control of neuronal excitability, providing a mechanistic link between Bayesian inference and cortical circuit dynamics.