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

Spike-timing-dependent plasticity and relevant mutual information maximization.

Gal Chechik1

  • 1Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel. ggal@cs.huji.ac.il

Neural Computation
|June 21, 2003
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 foundation model for continuous glucose monitoring data.

Nature·2026
Same author

Invariant Temporal Dynamics Underlie Perceptual Stability in Human Visual Cortex.

Current biology : CB·2017
Same author

On Expression Patterns and Developmental Origin of Human Brain Regions.

PLoS computational biology·2016
Same author

Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls.

Alzheimer's & dementia (Amsterdam, Netherlands)·2016
Same author

Gene Expression Switching of Receptor Subunits in Human Brain Development.

PLoS computational biology·2015
Same author

A unifying principle underlying the extracellular field potential spectral responses in the human cortex.

Journal of neurophysiology·2015

This study derives a novel spike-dependent learning rule for neurons, maximizing information transfer. The biologically feasible rule closely matches experimental observations, offering insights into synaptic plasticity mechanisms.

Area of Science:

  • Computational Neuroscience
  • Neuroscience
  • Information Theory

Background:

  • Synaptic plasticity, the ability of synapses to strengthen or weaken over time, is crucial for learning and memory.
  • The precise timing of pre- and postsynaptic spikes significantly influences synaptic plasticity.
  • Existing models often lack a direct link between information maximization and biological feasibility.

Purpose of the Study:

  • To analytically derive a spike-dependent learning rule based on information maximization.
  • To transform this rule into a biologically plausible model.
  • To compare the derived rule with experimentally observed synaptic plasticity.

Main Methods:

  • Analytical derivation of a learning rule using information maximization principles.

Related Experiment Videos

  • Transformation of the theoretical rule into a biologically feasible form.
  • Comparison of the model's predictions with experimental data on synaptic plasticity.

    • Main Results:

    • The derived biologically feasible rule approaches near-optimal information levels.
    • The model explains the temporal dependency of synaptic potentiation, influenced by synaptic transfer function and membrane leak.
    • Synaptic depression is suggested to handle rare inputs while unlearning baseline network statistics.

    Conclusions:

    • The derived spike-timing-dependent plasticity rule offers a computational explanation for biological mechanisms.
    • Synaptic potentiation involves weight-dependent and independent components of similar magnitude.
    • The study provides insights into the structure and function of synaptic plasticity, linking information theory to neural computation.