From basis functions to basis fields: Vector field approximation from sparse data

DOI10.1007/BF00198755zbMath0758.92004OpenAlexW2003292177WikidataQ30887491 ScholiaQ30887491MaRDI QIDQ1202326

Ferdinando A. Mussa-Ivaldi

Publication date: 23 February 1993

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00198755

zbMATH Keywords

learningcoordinate transformationsvector-valued mappingsbasis fieldscomponent- based representationlinearly independent vector fieldssensory informationvector-field processing

Mathematics Subject Classification ID

Neural biology (92C20) Memory and learning in psychology (91E40) Computational methods for problems pertaining to biology (92-08)

Related Items (6)

Identifying finite-time coherent sets from limited quantities of Lagrangian data ⋮ Multi-output learning via spectral filtering ⋮ Vector field approximation: A computational paradigm for motor control and learning ⋮ A parametric divergence-free vector field method for the optimization of composite structures with curvilinear fibers ⋮ Estimates of the approximation error using Rademacher complexity: Learning vector-valued functions ⋮ Computational physics of the mind

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• Vector field approximation: A computational paradigm for motor control and learning
• The convergence of sequences of rational functions of best approximation
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Neural networks and physical systems with emergent collective computational abilities.

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