Title: Neural mechanisms for sensory prediction in a cerebellum-like structure
Description: Any animal must be able to predict and cancel the sensory consequences of its own movements to avoid ambiguity in the origin of sensory input. Theoretical and human behavioral studies suggest that nervous systems contain internal models that use copies of outgoing motor signals along with incoming sensory feedback to predict the consequences of movements. Many studies propose the cerebellum as one possible site of such internal models. Yet whether such an internal model exists and how such an internal model might be implemented in neural circuits is largely speculative. Early work in cerebellum-like structures of mormyrid fish identified neural mechanisms of sensory predictions at the levels of synapses, cells, and circuits, and successfully linked those mechanisms to the systems-level function--the cancellation of electrosensory input due to the fish's own behavior. However, those early studies were restricted to predicting and cancelling the electrosensory consequences of relatively simple and rather specialized electromotor behavior.

The research described here takes an in vivo electrophysiological approach to generalize the previous work in mormyrid fish to the more ubiquitous problem of predicting and cancelling the sensory consequences of movements. First, I demonstrate that neurons in the electrosensory lobe of weakly electric mormyrid fish generate predictions at the cellular level, termed negative images, about the sensory consequences of the fish's own movements based on ascending spinal corollary discharge signals. Second, I examine the interactions between corollary discharge and proprioceptive feedback under conditions that simulate real movements. Using experiments and modeling, I show that plasticity acting on random, nonlinear mixtures of corollary discharge and proprioceptive signals can account for key properties of negative images observed in vivo. Mossy fibers originating in the spinal cord carry randomly mixed, though linear, corollary discharge and proprioceptive signals, while properties of granule cells observed in vivo are consistent with a nonlinear re-coding of these signals.

The conclusion of these studies is that both corollary discharge and proprioception, in combination with an associative neural network endowed with synaptic plasticity, provide a powerful and flexible basis for solving the ubiquitous problems of predicting the sensory consequences of movements.