Computational Advantages of Reverberating Loops for Sensorimotor Learning

DOI10.1162/NECO_a_00237zbMath1237.92013OpenAlexW2111737714WikidataQ51496263 ScholiaQ51496263MaRDI QIDQ2885109

Kristen Fortney, Douglas B. Tweed

Publication date: 21 May 2012

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00237

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Neural biology (92C20)

Related Items (1)

Biomechanical simulation and control of hands and tendinous systems

Cites Work

• Multilayer feedforward networks are universal approximators
• On the mathematical foundations of learning
• Sensitivity Derivatives for Flexible Sensorimotor Learning
• Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
• The Kernel Recursive Least-Squares Algorithm
• Learning representations by back-propagating errors
• SOME THEOREMS IN LEAST SQUARES
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