Data-driven Koopman operator approach for computational neuroscience

DOI10.1007/s10472-019-09666-2zbMath1466.37073OpenAlexW2986903508MaRDI QIDQ2023876

Dimitrios Giannakis, Heather L. Read, Joanna Slawinska, Natasza Marrouch

Publication date: 3 May 2021

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-019-09666-2

zbMATH Keywords

nonlinearspectral decompositionspatio-temporal dynamicsbrainKoopman operatormismatch negativity

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Dynamical systems in biology (37N25) Ergodic theory of linear operators (47A35) Time series analysis of dynamical systems (37M10) Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25) Spectral theory; eigenvalue problems on manifolds (58C40) Kernel functions in one complex variable and applications (30C40)

Related Items (6)

Modern Koopman Theory for Dynamical Systems ⋮ Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning ⋮ Cognition and neurocomputation ⋮ On Koopman mode decomposition and tensor component analysis ⋮ Data-driven operator theoretic methods for phase space learning and analysis ⋮ Koopman-based spectral clustering of directed and time-evolving graphs

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• Nonlinear Laplacian spectral analysis: capturing intermittent and low‐frequency spatiotemporal patterns in high‐dimensional data
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