Learning active sensing strategies using a sensory brain-machine interface

Our sensory experience is governed by sensor properties (e.g., eye photoreceptors) and corresponding motor strategies to sample the environment (e.g., eye movements). With injury, aging, or new task constraints, existing strategies can become incompatible with perceptual demands. Using a brain-machine interface paradigm in rats, we studied how motor strategies are adapted to new sensory inputs to accomplish a difficult searching task. We show that the strategies can be dynamically regulated through experience to optimally extract task-relevant sensory information.