Adaptive signal processing
Stable adaptive systems
Multilayer feedforward networks are universal approximators
Neural Networks
A cerebellar model of timing and prediction in the control of reaching
Neural Computation
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
Performance of Nonlinear Approximate Adaptive Controllers
Performance of Nonlinear Approximate Adaptive Controllers
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
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
IEEE Transactions on Information Theory
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When we learn something new, our brain may store the information in synapses or in reverberating loops of electrical activity, but current theories of motor learning focus almost entirely on the synapses. Here we show that loops could also play a role and would bring advantages: loop-based algorithms can learn complex control tasks faster, with exponentially fewer neurons, and avoid the problem of weight transport. They do all this at a cost: in the presence of long feedback delays, loop algorithms cannot control very fast movements, but in this case, loop and synaptic mechanisms can complement each other-mixed systems quickly learn to make accurate but not very fast motions and then gradually speed up. Loop algorithms explain aspects of consolidation, the role of attention, and the relapses that are sometimes seen after a task has apparently been learned, and they make further predictions.