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To learn effectively, an adaptive controller needs to know its sensitivity derivatives--the variables that quantify how system performance depends on the commands from the controller. In the case of biological sensorimotor control, no one has explained how those derivatives themselves might be learned, and some authors suggest they are not learned at all(More)
To learn, a motor system needs to know its sensitivity derivatives, which quantify how its neural commands affect motor error. But are these derivatives themselves learned, or are they known solely innately? Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback. In its simplest form, the(More)
In control theory, variables called sensitivity derivatives quantify how a system's performance depends on the commands from its controller. Knowledge of these derivatives is a prerequisite for adaptive control, including sen-sorimotor learning in the brain, but no one has explained how the derivatives themselves could be learned by real neural networks,(More)
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