An Internal Model of Sensorimotor Context in Freely Swimming Electric Fish
Internal models that predict the sensory consequences of motor actions are vital for sensory, motor, and cognitive functions. However, under real-world conditions, the relationships between motor action and sensory input are complex and may vary moment-to-moment depending on the environmental context. At the neural circuit level, little is known about how predictions are generated under such challenging conditions. Using novel methods for underwater neural recording, quantitative analysis of unconstrained behavior, and computational modeling, I’ve found evidence for an unexpectedly sophisticated internal model at the first stage of active electrosensory processing in mormyrid fish. Closed-loop manipulations reveal that individual electrosensory lobe neurons are capable of learning and storing multiple context-specific predictions of the sensory consequences of motor commands. These results provide mechanistic insights into how internal, motor-related signals and information about environmental context are combined within cerebellum-like circuitry to predict the sensory consequences of natural behavior. Finally, I will discuss using the electric fish as a model for studying the interactions between low-level sensorimotor circuits and high-level memory and decision-making circuits.