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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
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Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

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Modeling signal transduction in classical conditioning with network motifs.

Joyce Keifer1, James C Houk

  • 1Neuroscience Group, Division of Basic Biomedical Sciences, University of South Dakota Sanford School of Medicine Vermillion, SD, USA.

Frontiers in Molecular Neuroscience
|July 23, 2011
PubMed
Summary

Network motifs reveal a coherent feed-forward loop (C1-FFL) organizing signal transduction during classical conditioning. This network structure, involving AMPA receptor (AMPAR) trafficking, acts as a delay element and coincidence detector for learning.

Keywords:
AMPA receptor traffickingclassical conditioningeyeblinkfeed-forward loopsin vitromodelnetwork motifssignal transduction

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Area of Science:

  • Neuroscience
  • Systems Biology
  • Molecular Biology

Background:

  • Biological networks utilize recurring patterns called network motifs.
  • Standard signal transduction models struggle with the complexity of synaptic plasticity and learning.
  • Network motifs offer a novel approach to understanding these complex molecular interactions.

Purpose of the Study:

  • To apply network motifs to model signal transduction in classical conditioning.
  • To reveal the underlying molecular organization of synaptic plasticity and learning.
  • To investigate the role of AMPA receptor trafficking in learned responses.

Main Methods:

  • Modeling signal transduction pathways using network motifs.
  • Analyzing molecular interactions during in vitro eyeblink classical conditioning.
  • Identifying network structures like coherent feed-forward loops (C1-FFL).

Main Results:

  • Identified a C1-FFL with AND logic governing two stages of AMPA receptor (AMPAR) trafficking.
  • Demonstrated that GluR1 and PDK-1 co-activation drives GluR4 delivery and conditioned response acquisition.
  • Characterized the FFL as a sign-sensitive delay element and coincidence detector.

Conclusions:

  • Network motifs provide a powerful framework for dissecting signal transduction in learning.
  • The identified FFL architecture explains the non-linearity and temporal dynamics of conditioning.
  • This motif-based approach can unify findings across different learning systems and models.