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 memory dynamics

E W Kairiss1, W L Miranker

  • 1Department of Psychology, Yale University, New Haven, Connecticut, USA.

Biological Cybernetics
|July 4, 1998
PubMed
Summary
This summary is machine-generated.

This study models biological memory using hierarchical layers, lateral inhibition, and Hebbian learning. The model demonstrates memory feasibility and recall dynamics, offering insights into neural network behavior.

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

Competition and cooperation in neuronal processing.

IEEE transactions on neural networks·2008
Same author

Self-organization of an oscillatory neural system.

Journal of mathematical biology·1995
Same author

Electrophysiology and morphology of neurons in rat perirhinal cortex.

Brain research·1994
Same author

Quantal mechanism of long-term potentiation in hippocampal mossy-fiber synapses.

Journal of neurophysiology·1994
Same author

Neural organization of the locomotive oscillator.

Biological cybernetics·1993
Same author

Multiscale optimization in neural nets.

IEEE transactions on neural networks·1991
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

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

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles

Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Cognitive Science

Background:

  • Biological memory exhibits hierarchical, interacting layers, lateral inhibition, and Hebbian synaptic plasticity.
  • These features are crucial for understanding memory formation and retrieval in neural systems.

Purpose of the Study:

  • To develop a mathematical and computational model incorporating key features of biological memory.
  • To analyze Hebbian learning and recall dynamics within this model.
  • To investigate memory feasibility, stability, and performance using an infomax perspective.

Main Methods:

  • Development of a mathematical and computational model of biological memory.
  • Derivation and analysis of Hebbian learning and recall dynamics.

Related Experiment Videos

  • Application of analytical and computational methods to study memory stability and convergence.
  • Utilizing the infomax principle to evaluate memory performance and characterize recall.

    • Main Results:

    • A conservation law for memory feasibility under Hebbian dynamics was derived.
    • The model's recall dynamics were characterized, including behavior with noisy cues.
    • Infomax principles were used to identify optimal memory solutions and assess recall accuracy.
    • Initial state sensitivity in dynamical behavior was observed, suggesting biological parallels.

    Conclusions:

    • The developed model successfully incorporates key features of biological memory, providing a framework for studying its dynamics.
    • The infomax perspective offers a valuable metric for evaluating memory performance and optimizing neural network solutions.
    • The model's findings, including initial state sensitivity, offer potential insights into the biological mechanisms of memory and recall.