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

Cortical reorganization consistent with spike timing-but not correlation-dependent plasticity.

Joshua M Young1, Wioletta J Waleszczyk, Chun Wang

  • 1Neural Information Processing Group, Department of Electrical Engineering and Computer Science, Berlin University of Technology, FR 2-1, Franklinstrasse 28/29, D-10587 Berlin, Germany.

Nature Neuroscience
|May 29, 2007
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

Receptive Field Properties of Koniocellular On/Off Neurons in the Lateral Geniculate Nucleus of Marmoset Monkeys.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2018
Same author

Projections of three subcortical visual centers to marmoset lateral geniculate nucleus.

The Journal of comparative neurology·2018
Same author

Silencing "Top-Down" Cortical Signals Affects Spike-Responses of Neurons in Cat's "Intermediate" Visual Cortex.

Frontiers in neural circuits·2017
Same author

Binocular Visual Responses in the Primate Lateral Geniculate Nucleus.

Current biology : CB·2016
Same author

The effects of erythropoiesis-stimulating agents on the short-term and long-term survivals in metastatic breast cancer patients receiving chemotherapy: a SEER population-based study.

Breast cancer research and treatment·2015
Same author

Inhibition of microRNA-29c protects the brain in a rat model of prolonged hypothermic circulatory arrest.

The Journal of thoracic and cardiovascular surgery·2015

Neurons in the visual cortex can reorganize after retinal damage. This study reveals that this reorganization depends on the order, not just the timing, of neural signals, suggesting a new mechanism for neural network learning.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual System Research

Background:

  • Neurons in the primary visual cortex (V1) exhibit plasticity, with receptive fields shifting to undamaged retinal areas after injury.
  • These shifts are observed to be highly convergent, even for V1 receptive fields initially distant from each other.

Purpose of the Study:

  • To investigate the mechanism underlying the convergent receptive field shifts in V1 following retinal inactivation.
  • To test the consistency of observed shifts with models of synaptic plasticity based on temporal correlation versus temporal order of neural activity.

Main Methods:

  • Development and utilization of a computational model simulating the primary visual cortex.
  • Analysis of receptive field shifts in response to simulated retinal damage within the model.

Related Experiment Videos

  • Comparison of model predictions with experimental observations in cats.

    • Main Results:

    • Convergent receptive field shifts are inconsistent with plasticity rules based solely on the temporal correlation of pre- and postsynaptic action potentials.
    • The observed convergent shifts are consistent with a plasticity mechanism dependent on the temporal order of neural activity.
    • The network reorganization appears to necessitate increased neuronal gain.

    Conclusions:

    • Synaptic plasticity in the visual cortex, particularly in response to damage, may be driven by the temporal order of neuronal firing rather than just temporal correlation.
    • This temporal order-dependent plasticity, coupled with increased neuronal gain, offers a potential mechanism for efficient transfer of neuronal response properties in neural networks.
    • Findings challenge existing models and propose a refined understanding of neural plasticity and network adaptation.