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

Dynamical model of long-term synaptic plasticity.

Henry D I Abarbanel1, R Huerta, M I Rabinovich

  • 1Marine Physical Laboratory, Scripps Institution of Oceanography, Department of Physics, Institute for Nonlinear Science, University of California at San Diego, La Jolla, CA 93093-0402, USA. harbaranel@ucsd.edu

Proceedings of the National Academy of Sciences of the United States of America
|July 13, 2002
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

Model of the HVC neural network as a song motor in zebra finch.

Frontiers in computational neuroscience·2024
Same author

Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers.

Chaos (Woodbury, N.Y.)·2023
Same author

A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics.

Neural networks : the official journal of the International Neural Network Society·2022
Same author

Reduced-Dimension, Biophysical Neuron Models Constructed From Observed Data.

Neural computation·2022
Same author

Robust forecasting using predictive generalized synchronization in reservoir computing.

Chaos (Woodbury, N.Y.)·2022
Same author

A personal retrospective on the 60th anniversary of the journal biological cybernetics.

Biological cybernetics·2021
Same journal

In This Issue.

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

Long-term cultural continuity across the Neanderthal-modern human sequence at Üçağızlı II Cave, northern Levant.

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

Dolphins use names to remember whom to avoid.

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

Retraction for Shaked and Frenkel, Curiouser and curiouser: Meningeal lymphoid structures in the aging brain.

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

Small but mighty: The outsized role of small water bodies in the global carbon cycle.

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

Functional traits produce conditional outcomes in different community contexts.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

This study introduces a two-component model for synaptic plasticity, explaining how timing between neural events influences learning and memory. The model accurately predicts long-term potentiation (LTP) and long-term depression (LTD) based on spike timing and frequency.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Synaptic Plasticity

Background:

  • Long-term synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), is crucial for learning and memory.
  • Synaptic plasticity is induced by precise timing of pre- and postsynaptic events and influenced by calcium ion dynamics.
  • Existing models describe synaptic plasticity but a unified phenomenological description is needed.

Purpose of the Study:

  • To present a phenomenological, two-component dynamical model for synaptic plasticity.
  • To reproduce spike-time-dependent plasticity (STDP) observed in excitatory synapses.
  • To connect the model with the Bienenstock, Cooper, and Munro (BCM) rule and predict plasticity changes under various spike train frequencies.

Main Methods:

Related Experiment Videos

  • Developed a two-component phenomenological model for synaptic plasticity.
  • Simulated the model under conditions of voltage-clamped postsynaptic cells (depolarized for LTP, hyperpolarized for LTD).
  • Investigated model responses to periodic and Poisson-distributed spike trains of varying frequencies.

    • Main Results:

    • The model successfully reproduces spike-time-dependent plasticity based on the relative timing of pre- and postsynaptic events.
    • It accurately predicts LTP and LTD induction under specific voltage-clamp conditions.
    • Model predictions show that frequencies exceeding approximately 30-40 Hz consistently induce LTP.

    Conclusions:

    • The proposed two-component model offers a unified phenomenological description of synaptic plasticity.
    • It provides a framework for understanding how spike timing and frequency govern LTP and LTD.
    • The model's ability to connect with the BCM rule and predict plasticity changes enhances its explanatory power for neural learning mechanisms.