Neural basis of causal inference: representations, circuits, and dynamics
Project Summary The same pattern of neural activity can correspond to multiple events in the world. Signals sweeping across the retina, for instance, might be generated by a moving object or by the animal's self-motion. The brain resolves this ambiguity by inferring what events best explain sensory activity. This process, called causal inference, is a foundation of action-perception loops in all sensory-motor systems. To support adaptive action, neural representations of variables involved in these computations should be internally consistent.