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

Extremely dilute modular neuronal networks: neocortical memory retrieval dynamics.

Carlo Fulvi Mari1

  • 1Department of Mathematical Sciences, Loughborough University, United Kingdom; Department of Psychology, University of Liège, Belgium. fulvi@sissa.it

Journal of Computational Neuroscience
|June 26, 2004
PubMed
Summary

This study models neocortical association areas using modular networks. An oscillatory retrieval process effectively overcomes memory retrieval errors caused by shared features.

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

Memory retrieval dynamics and storage capacity of a modular network model of association cortex with featural decomposition.

Bio Systems·2021
Same author

Inferring population statistics of receptor neurons sensitivities and firing-rates from general functional requirements.

Bio Systems·2020
See all related articles

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Cognitive modeling

Background:

  • Neocortical association areas process complex information.
  • Hebbian learning and modular networks are key to memory.
  • Feature sharing in networks can lead to retrieval errors.

Purpose of the Study:

  • To model columnar networks of neocortical association areas.
  • To investigate memory retrieval dynamics in large modular networks.
  • To propose an efficient retrieval process overcoming feature-sharing drawbacks.

Main Methods:

  • Mathematical analysis of a modular network model.
  • Numerical simulations of network dynamics.
  • Investigating an oscillatory retrieval process.

Related Experiment Videos

Main Results:

  • An oscillatory retrieval process efficiently overcomes feature-sharing errors.
  • Exploiting statistical correlations in features enhances memory retrieval.
  • Activity 'islands' limit retrieval quality, but the proposed process nearly saturates this bound.

Conclusions:

  • The proposed oscillatory retrieval dynamics are highly effective in modular networks.
  • Network architecture and feature statistics influence retrieval efficiency.
  • Understanding these dynamics advances models of memory and cognition.