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

Trace feature map: a model of episodic associative memory.

R Miikkulainen1

  • 1Department of Computer Sciences, University of Texas, Austin 78712-1188.

Biological Cybernetics
|January 1, 1992
PubMed
Summary

This study introduces a novel approach to episodic associative memory using topological feature maps. This model mimics human memory by storing and retrieving episodic traces, showing graceful performance degradation and recall biases for recent or unique memories.

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

Constrained emergence of universals and variation in syllable systems.

Language and speech·2001
Same author

Tilt aftereffects in a self-organizing model of the primary visual cortex.

Neural computation·2000
Same author

Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex.

Neural computation·1997
Same author

CHANGE OF SEX IN MALE PSEUDOHERMAPHRODITISM.

Annales paediatriae Fenniae·1963

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Human episodic associative memory involves recalling specific past events.
  • Existing computational models often struggle to replicate nuanced memory behaviors like interference and recall biases.
  • Topological feature maps offer a structured way to represent data, potentially applicable to memory modeling.

Purpose of the Study:

  • To present a novel computational approach for modeling human episodic associative memory.
  • To design a memory model with desirable properties such as plausible interference and graceful degradation.
  • To leverage topological feature maps for efficient episodic memory representation and retrieval.

Main Methods:

  • Utilizing topological feature maps to represent data, creating "trace feature maps".

Related Experiment Videos

  • Implementing episodic storage where single presentations generate memory traces.
  • Developing retrieval mechanisms using partial cues and incorporating overlap for interference.
  • Analyzing recall performance based on recency and uniqueness of traces.

    • Main Results:

    • The trace feature map model successfully stores episodic traces from single presentations.
    • Partial cues enable effective retrieval of stored episodic information.
    • Overlapping traces lead to realistic memory interference patterns.
    • Memory performance degrades gradually under overload, with recency and uniqueness influencing recall.

    Conclusions:

    • The proposed trace feature map approach offers a viable computational model for episodic associative memory.
    • The model successfully replicates key human memory characteristics, including interference and recall biases.
    • This framework provides a foundation for further research into artificial memory systems and cognitive modeling.