Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Stable irregular dynamics in complex neural networks.

Sven Jahnke1, Raoul-Martin Memmesheimer, Marc Timme

  • 1Network Dynamics Group, Max Planck Institute for Dynamics & Self-Organization (MPIDS), Göttingen, Germany.

Physical Review Letters
|March 21, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sustainability as a challenge in complex systems dynamics.

Nature computational science·2026
Same author

Cooperative coding of continuous variables in networks with sparsity constraint.

PLoS computational biology·2025
Same author

Extreme synchronization transitions.

Nature communications·2025
Same author

Smooth Exact Gradient Descent Learning in Spiking Neural Networks.

Physical review letters·2025
Same author

Perturbation-response dynamics of coupled nonlinear systems.

Chaos (Woodbury, N.Y.)·2024
Same author

Efficient self-organization of informal public transport networks.

Nature communications·2024
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Irregular dynamics in spiking neural networks, often linked to chaos, can be stable. This study shows that even highly irregular activity in finite inhibitory networks converges to stable, periodic patterns, not chaos.

Area of Science:

  • Computational neuroscience
  • Complex systems dynamics
  • Network theory

Background:

  • Irregular dynamics in multidimensional systems are often attributed to chaos.
  • Mean field theory suggests balanced states in large sparse spiking neural networks exhibit irregular activity.
  • Microscopic analysis of finite networks is needed to understand underlying dynamics.

Purpose of the Study:

  • To analytically investigate the microscopic irregular dynamics in finite spiking neural networks.
  • To determine if irregular dynamics in balanced states are chaotic or stable.
  • To analyze networks with delayed, purely inhibitory interactions.

Main Methods:

  • Analytical investigation of microscopic dynamics.
  • Tracking individual spike times in finite networks.

Related Experiment Videos

  • Focus on delayed, purely inhibitory neural interactions.

    • Main Results:

    • Irregular dynamics in finite inhibitory networks are not chaotic.
    • The observed irregular dynamics are stable and converge towards periodic orbits.
    • Chaotic and stable dynamics can exhibit similar levels of irregularity.

    Conclusions:

    • The balanced state in finite inhibitory networks does not necessarily imply chaotic dynamics.
    • Stable, periodic dynamics can manifest as highly irregular activity.
    • Distinguishing between chaotic and stable irregular dynamics requires microscopic analysis.