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Related Experiment Video

Updated: Feb 27, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

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Complex behavior in chains of nonlinear oscillators.

Leandro M Alonso1

  • 1New York, New York 10028, USA.

Chaos (Woodbury, N.Y.)
|July 7, 2017
PubMed
Summary
This summary is machine-generated.

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Complex behavior in nonlinear oscillator networks emerges from local interactions and excitability. This critical state, balanced by excitation and inhibition, leads to intricate spatio-temporal patterns and high-dimensional bifurcations.

Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Network Science

Background:

  • Investigating emergent complex behavior in systems of interacting units.
  • Understanding the role of excitability and local interactions in network dynamics.

Purpose of the Study:

  • To identify conditions leading to complex spatio-temporal behavior in one-dimensional nonlinear oscillator chains.
  • To analyze the influence of balanced excitation and inhibition on network dynamics.

Main Methods:

  • Modeling a one-dimensional chain of identical, excitable nonlinear oscillators.
  • Describing units using phase equations with local interactions.
  • Analyzing system behavior at a critical state balanced by excitation and inhibition.

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Related Experiment Videos

Last Updated: Feb 27, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

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Generation of Local CA1 γ Oscillations by Tetanic Stimulation
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Generation of Local CA1 γ Oscillations by Tetanic Stimulation

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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

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Main Results:

  • Demonstrated that excitability and balanced local interactions are sufficient for complex emergent features in oscillatory networks.
  • Observed complex spatio-temporal behavior under specific parameter regimes.
  • Identified high-dimensional bifurcations creating numerous equilibria via saddle-node bifurcations.

Conclusions:

  • Excitability and balanced local interactions are key drivers of complex dynamics in large oscillatory networks.
  • The findings provide insights into the emergence of complex behavior from simple, locally interacting components.