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

Attractor reliability reveals deterministic structure in neuronal spike trains.

P H E Tiesinga1, J-M Fellous, Terrence J Sejnowski

  • 1Sloan-Swartz Center for Theoretical Neurobiology and Computational Neurobiology Lab, Salk Institute, La Jolla, CA 92037, USA. tiesinga@salk.edu

Neural Computation
|June 25, 2002
PubMed
Summary

Neurons exhibit reproducible spike sequences due to attractor dynamics, indicating high reliability for spike-time coding. This reliability surpasses renewal processes, even with similar spike patterns.

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

Transition of the presynaptic vesicle cluster from a compact to dispersed organization during long-term potentiation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Synaptic spine head morphodynamics from graph grammar rules for actin dynamics.

bioRxiv : the preprint server for biology·2026
Same author

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same author

Balancing Stability and Flow in Hippocampal Networks via Inductive Bias and Learned Symmetry Breaking.

bioRxiv : the preprint server for biology·2026
Same author

A Spatiotemporal Perspective on Dynamical Computation in Neural Information Processing Systems.

ArXiv·2026
Same author

Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks.

Neural computation·2026

Area of Science:

  • Computational Neuroscience
  • Neuroscience

Background:

  • Neurons exhibit complex electrical activity in response to stimuli.
  • The integrate-and-fire model is a simplified representation of neuronal firing.
  • Neuronal responses can be characterized by spike trains, which may exhibit reproducible patterns.

Purpose of the Study:

  • To introduce and quantify a new measure, attractor reliability, for neuronal spike trains.
  • To differentiate between spike trains with high reliability (attractor dynamics) and those following a renewal process.
  • To investigate the reliability of spike trains in cortical neurons in vitro.

Main Methods:

  • Simulated periodic current injection into an integrate-and-fire model neuron.
  • Quantified attractor reliability as the inverse of distinct spike trains from repeated stimuli.

Related Experiment Videos

  • Developed a new method for calculating attractor reliability.
  • Recorded and analyzed spike trains from cortical neurons in vitro subjected to current injection.

    • Main Results:

    • Periodic current injection leads to voltage attractors producing reproducible spike sequences.
    • Attractor reliability measures spike train stability against intrinsic noise.
    • High attractor reliability is indicative of spike-time coding, distinct from renewal processes.
    • Cortical neurons in vitro exhibited higher reliability than renewal-like processes with similar spike-time histograms.

    Conclusions:

    • Attractor dynamics provide a robust method for identifying reliable spike trains and spike-time coding.
    • The new attractor reliability measure distinguishes between different neuronal response types, which spike-time histograms alone cannot.
    • Cortical neurons demonstrate high attractor reliability, supporting their role in precise temporal coding.