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

Desynchronization in diluted neural networks.

Rüdiger Zillmer1, Roberto Livi, Antonio Politi

  • 1INFN Sezione Firenze, via Sansone 1, I-50019 Sesto Fiorentino, Italy. zillmer@fi.infn.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 10, 2006
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

A detailed investigation of Shared Variance Component Analysis as a tool to characterize neural dimensionality.

Journal of neuroscience methods·2026
Same author

A theory for self-sustained balanced states in absence of strong external currents.

PLoS computational biology·2026
Same author

Entropy transfer from solar radio bursts to energetic particles.

Science advances·2025
Same author

Synaptic shot noise triggers fast and slow global oscillations in balanced neural networks.

Physical review. E·2025
Same author

Crisis in Time-Dependent Dynamical Systems.

Physical review letters·2025
Same author

Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP.

PLoS computational biology·2025
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

This study explores spiking neural networks, revealing a transition from regular to stochastic-like dynamics with increased coupling strength. The irregular patterns observed are a transient phenomenon, analogous to stable chaos.

Area of Science:

  • Computational Neuroscience
  • Complex Systems Dynamics
  • Network Science

Background:

  • Investigating the dynamics of pulse-coupled spiking neural networks is crucial for understanding brain function.
  • Fully inhibitory networks offer a simplified yet powerful model for studying emergent network behaviors.

Purpose of the Study:

  • To analyze the dynamical behavior of a weakly diluted, fully inhibitory network of pulse-coupled spiking neurons.
  • To characterize the transition from regular to stochastic-like dynamics as coupling strength increases.

Main Methods:

  • Numerical simulations of spiking neural network models.
  • Analysis of dynamical regimes, including firing rates, phase locking, and Lyapunov exponents.
  • Investigation of transient dynamics and their properties.

Related Experiment Videos

Main Results:

  • A transition from a regular to a stochastic-like regime was observed with increasing coupling strength.
  • In the weak-coupling phase, neurons exhibited periodic dynamics with synchronized firing rates and mutual phase locking.
  • The strong-coupling phase showed irregular firing patterns, despite a negative maximum Lyapunov exponent, explained by transient dynamics analogous to stable chaos.

Conclusions:

  • The observed irregular dynamics in strong-coupling regimes are a stationary transient phenomenon, not true chaos.
  • This finding provides insights into the complex behaviors that can emerge in biological and artificial neural networks.
  • The concept of stable chaos helps resolve the apparent paradox of irregular dynamics in systems with negative Lyapunov exponents.