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Neuronal synchrony does not correlate with motion coherence in cortical area MT

Author

Listed:
  • Alexander Thiele

    (Salk Institute for Biological Studies
    University of Newcastle upon Tyne)

  • Gene Stoner

    (Salk Institute for Biological Studies)

Abstract

Natural visual scenes are cluttered with multiple objects whose individual features must somehow be selectively linked (or ‘bound’) if perception is to coincide with reality. Recent neurophysiological evidence1,2 supports a ‘binding-by-synchrony’ hypothesis3: neurons excited by features of the same object fire synchronously, while neurons excited by features of different objects do not. Moving plaid patterns offer a straightforward means to test this idea. By appropriate manipulations of apparent transparency, the component gratings of a plaid pattern can be seen as parts of a single coherently moving surface or as two non-coherently moving surfaces. We examined directional tuning and synchrony of area-MT neurons in awake, fixating primates in response to perceptually coherent and non-coherent plaid patterns. Here we show that directional tuning correlated highly with perceptual coherence, which is consistent with an earlier study4. Although we found stimulus-dependent synchrony, coherent plaids elicited significantly less synchrony than did non-coherent plaids. Our data therefore do not support the binding-by-synchrony hypothesis as applied to this class of motion stimuli in area MT.

Suggested Citation

  • Alexander Thiele & Gene Stoner, 2003. "Neuronal synchrony does not correlate with motion coherence in cortical area MT," Nature, Nature, vol. 421(6921), pages 366-370, January.
  • Handle: RePEc:nat:nature:v:421:y:2003:i:6921:d:10.1038_nature01285
    DOI: 10.1038/nature01285
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    Cited by:

    1. Andrew M. Clark & David C. Bradley, 2022. "A neural correlate of perceptual segmentation in macaque middle temporal cortical area," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Rory G Townsend & Pulin Gong, 2018. "Detection and analysis of spatiotemporal patterns in brain activity," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-29, December.

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