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Multifrequency Hebbian plasticity in coupled neural oscillators

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Publication:2659630
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DOI10.1007/s00422-020-00854-6zbMath1460.92013OpenAlexW3120158846MaRDI QIDQ2659630

Ji Chul Kim, Edward W. Large

Publication date: 26 March 2021

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

Full work available at URL: https://doi.org/10.1007/s00422-020-00854-6


zbMATH Keywords

neural networksynchronizationnonlinear resonanceneural oscillationHebbian plasticity


Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)




Cites Work

  • Unnamed Item
  • A canonical model for gradient frequency neural networks
  • Nonlinear oscillations, dynamical systems, and bifurcations of vector fields
  • Mathematical formulations of Hebbian learning
  • Synaptic organizations and dynamical properties of weakly connected neural oscillators. I: Analysis of a canonical model
  • Synaptic organizations and dynamical properties of weakly connected neural oscillators. II: Learning phase information
  • On the non-linear mechanics of hydrodynamic stability
  • Hierarchical transitions in multiplex adaptive networks of oscillatory units
  • Normal Forms and Unfoldings for Local Dynamical Systems
  • Elements of applied bifurcation theory
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