Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation
Automatica (Journal of IFAC)
Mathematical control theory: deterministic finite dimensional systems (2nd ed.)
Mathematical control theory: deterministic finite dimensional systems (2nd ed.)
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Introduction to Neural and Cognitive Modeling
Introduction to Neural and Cognitive Modeling
Optimal Control: Basics and Beyond
Optimal Control: Basics and Beyond
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
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Sensitivity derivatives for flexible sensorimotor learning
Neural Computation
Kernel Adaptive Filtering: A Comprehensive Introduction
Kernel Adaptive Filtering: A Comprehensive Introduction
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
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Many neural control systems are at least roughly optimized, but how is optimal control learned? There are algorithms for this purpose, but in their current forms, they are not suited for biological neural networks because they rely on a type of communication that is not available in the brain, namely, weight transport-transmitting the strengths, or "weights," of individual synapses to other synapses and neurons. Here we show how optimal control can be learned without weight transport. Our method involves a set of simple mechanisms that can compensate for the absence of weight transport in the brain and so may be useful for neural computation generally.