Inverse Problems in Neural Field Theory

DOI10.1137/080731220zbMath1402.92026OpenAlexW2062927779MaRDI QIDQ3643417

Peter beim Graben, Roland W. E. Potthast

Publication date: 11 November 2009

Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)

Full work available at URL: http://centaur.reading.ac.uk/29359/1/2009_Potthast_Graben_SIADS_IP_Neural_Field_Theory.pdf

zbMATH Keywords

inverse problemTikhonov regularizationHebbian learning ruleneural field theorykernel constructionneural learningAmari equation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Memory and learning in psychology (91E40)

Related Items (19)

Asymmetry in neural fields: a spatiotemporal encoding mechanism ⋮ On matrices whose Moore–Penrose inverse is idempotent ⋮ On the spectra of certain integro-differential-delay problems with applications in neurodynamics ⋮ A Biological Gradient Descent for Prediction Through a Combination of STDP and Homeostatic Plasticity ⋮ A Novel Iterative Method for Computing Generalized Inverse ⋮ A scalable approach for variational data assimilation ⋮ Neural network for computing pseudoinverses and outer inverses of complex-valued matrices ⋮ An efficient method to compute the Moore-Penrose inverse ⋮ Geometric representations for minimalist grammars ⋮ Logic in a dynamic brain ⋮ On instabilities in data assimilation algorithms ⋮ Sparse network estimation for dynamical spatio-temporal array models ⋮ Kernel reconstruction for delayed neural field equations ⋮ Transformed and generalized localization for ensemble methods in data assimilation ⋮ An efficient quadratically convergent iterative method to find the Moore–Penrose inverse ⋮ Control of Nonlinear Wave Solutions to Neural Field Equations ⋮ Tutorial on Neural Field Theory ⋮ Universal Neural Field Computation ⋮ Neural Fields, Masses and Bayesian Modelling

This page was built for publication: Inverse Problems in Neural Field Theory