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Recurrent computations for visual pattern completion.

Hanlin Tang1,2, Martin Schrimpf2,3,4,5, William Lotter1,2,6

  • 1Program in Biophysics, Harvard University, Boston, MA 02115.

Proceedings of the National Academy of Sciences of the United States of America
|August 15, 2018
PubMed
Summary
This summary is machine-generated.

Pattern completion allows us to recognize objects from partial information. Recurrent computations in the brain are crucial for this visual inference, as demonstrated by psychophysics, physiology, and computational models.

Keywords:
artificial intelligencecomputational neurosciencemachine learningpattern completionvisual object recognition

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Visual Perception

Background:

  • Pattern completion is essential for recognizing objects with limited visibility or occlusion.
  • Understanding the neural mechanisms of pattern completion is key to understanding visual cognition.

Purpose of the Study:

  • To test the hypothesis that pattern completion relies on recurrent computations.
  • To investigate the role of recurrent processing in visual inference from partial information.

Main Methods:

  • Combined psychophysical experiments, invasive physiological recordings in the ventral cortex, and computational modeling.
  • Assessed object recognition under varying degrees of visibility and with backward masking.
  • Developed and tested feed-forward and recurrent computational models of visual recognition.

Main Results:

  • Human subjects recognized objects with <15% visibility, but backward masking impaired recognition.
  • Physiological responses in the ventral cortex showed delayed, visually selective responses to partially visible objects.
  • Recurrent computational models, unlike feed-forward ones, robustly handled partial visibility and predicted human performance, aligning with physiological data.

Conclusions:

  • Recurrent computations provide a plausible mechanism for pattern completion in visual perception.
  • These findings support the role of recurrent neural networks in enabling visual inferences from incomplete data.
  • The study links behavioral, physiological, and computational evidence for recurrent processing in object recognition.