Topic: Bayesian integration

Our daily endeavors occur in a complex visual environment, whose intrinsic variability shapes the way we integrate information to make decisions. By processing thousands of parallel sensory inputs, our brain is theoretically able to compute the uncertainty of its environment, which would allow it to perform Bayesian integration of its internal representations and its new sensory inputs to drive optimal inference. While there is convincing evidence that humans do compute this sensory uncertainty to guide their behavior, the actual neurobiological and computational principles on which uncertaint

Sensorimotor systems are inherently uncertain, either because of the physical nature of the sensory stimulus or because of noise within neural circuits. To curtail such limitations, sensorimotor systems combine information about the same event across different sensory sources to create robust internal representations for behavioral performance. Here we show that congruent multimodal self-movement signals are consistently found throughout populations of visual neurons, the lobula plate tangential cells (LPTCs). LPTCs monitor head and/or body rotations to support steering during locomotion1-3. W