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Related Experiment Videos

Transform-invariant recognition by association in a recurrent network

N Parga1, E Rolls

  • 1Oxford University, Department of Experimental Psychology, England.

Neural Computation
|August 11, 1998
PubMed
Summary

This study proposes that sustained neural activity from recurrent connections in the visual pathway creates a memory trace for object recognition. This mechanism allows the brain to recognize objects regardless of viewing angle or scale.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Object recognition is invariant to changes in viewpoint, scale, and retinal position.
  • A memory trace, linking consecutive views of an object, is hypothesized to underlie this invariance.
  • Sustained neural activity and recurrent connectivity are potential mechanisms for this memory trace.

Purpose of the Study:

  • To investigate if extensive recurrent connectivity in the visual pathway can provide the memory trace necessary for view-invariant object recognition.
  • To model how synaptic efficacies, based on associations between object views, contribute to sustained neural activity.
  • To analytically explore the network's behavior under varying association strengths.

Main Methods:

  • Development of a simplified analytical model of the visual pathway with high recurrent connectivity.

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  • Incorporation of synaptic efficacies derived from associations between pairs of object views.
  • Analysis of network dynamics as the relative strength of association between views increases.

    • Main Results:

    • A critical threshold in the association strength between views was identified.
    • Below the threshold, sustained activity is driven by individual views.
    • Above the threshold, the network consistently converges to a stable object representation, irrespective of the initial stimulus view.

    Conclusions:

    • Extensive recurrent connectivity can generate a memory trace supporting view-invariant object recognition.
    • A specific regime of association strength enables the network to reliably represent objects.
    • The model demonstrates the capacity to store a large repertoire of objects, each defined by a finite set of views.