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Related Experiment Videos

Can Hebbian volume learning explain discontinuities in cortical maps?

G J Mitchison1, N V Swindale

  • 1Laboratory of Molecular Biology, Medical Research Council Centre, Hills Road, Cambridge, CB2 2QH, United Kingdom. gjm@mrc-lmb.cam.ac.uk

Neural Computation
|September 22, 1999
PubMed
Summary

Primary visual cortex maps show correlated jumps in orientation and position. A modified Kohonen algorithm with Hebbian learning produces these maps but with unrealistic features, suggesting alternative models for cortical map development.

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Area of Science:

  • Computational neuroscience
  • Neuroscience
  • Visual cortex mapping

Background:

  • Primary visual cortex contains maps of orientation and retinotopic position.
  • Observed correlated jumps in these maps challenge existing models like Kohonen's algorithm.
  • Kohonen's algorithm typically predicts anticorrelated changes in mapped variables.

Purpose of the Study:

  • To investigate if a Hebbian component can modify Kohonen's algorithm to produce correlated jumps.
  • To explore the implications of Hebbian learning for cortical map formation.
  • To evaluate the realism of maps generated by a Hebbian-modified Kohonen algorithm.

Main Methods:

  • Introduction of a Hebbian learning component into Kohonen's self-organizing map algorithm.
  • Modeling synaptic facilitation as dependent on both signal spread and postsynaptic activity.

Related Experiment Videos

  • Analysis of the resulting cortical maps for features like orientation singularities and ocular dominance columns.

    • Main Results:

    • The modified algorithm successfully generated correlated jumps in orientation and retinotopic position.
    • The algorithm produced maps with discontinuities and orientation singularities.
    • These singularities were unrealistically aligned with the edges of ocular dominance columns.

    Conclusions:

    • A Hebbian volume learning mechanism can explain correlated jumps in visual cortical maps.
    • The generated maps possess unrealistic features, indicating limitations of this specific Hebbian model.
    • Standard non-Hebbian volume learning, possibly combined with other mechanisms, may better model cortical map development and receptive field shifts.