Adaptive filter theory
Linear system theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
A fast learning algorithm for deep belief nets
Neural Computation
Computational advantages of reverberating loops for sensorimotor learning
Neural Computation
Adaptive optimal control without weight transport
Neural Computation
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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 but are known innately. Here we show that this knowledge cannot be solely innate, given the adaptive flexibility of neural systems. And we show how it could be learned using forms of information transport that are available in the brain. The mechanism, which we call implicit supervision, helps explain the flexibility and speed of sensorimotor learning and our ability to cope with high-dimensional work spaces and tools.