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Broken time-reversal symmetry in visual motion detection.

Nathan Wu1, Baohua Zhou2, Margarida Agrochao2

  • 1Yale College, New Haven, CT 06511.

Proceedings of the National Academy of Sciences of the United States of America
|March 6, 2025
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Summary

Biological motion detectors break time reversal symmetry, contrary to classical models. This study in fruit flies reveals this asymmetry arises from naturalistic visual input and neural network constraints, not just data properties.

Keywords:
Drosophilaalgorithmmotion detectionsymmetrysymmetry breaking

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Vision Science

Background:

  • Classical models of motion estimation assume perfect time reversal symmetry.
  • Biological visual systems are expected to mirror this symmetry in motion perception.

Purpose of the Study:

  • To investigate whether biological motion perception exhibits time reversal symmetry.
  • To identify stimuli and conditions that reveal symmetry breaking in visual systems.
  • To explore the role of neural network properties in time reversal symmetry breaking.

Main Methods:

  • Designed specific visual stimuli to test time reversal symmetry in fruit fly (Drosophila) optomotor behavior.
  • Trained neural network models to predict scene velocity using naturalistic and artificial contrast distributions.
  • Analyzed model responses analytically and numerically to identify sources of symmetry breaking.

Main Results:

  • Fruit fly behavioral responses demonstrated broken time reversal symmetry.
  • Neural network models trained on naturalistic visual data exhibited symmetry breaking, even with symmetric training data.
  • Contrast asymmetry and other features of the contrast distribution contribute to symmetry breaking.
  • Shallower neural networks showed stronger symmetry breaking than deeper ones.

Conclusions:

  • Biological motion detection is not perfectly symmetric upon time reversal.
  • Symmetry breaking likely arises from constrained optimization in natural environments.
  • Neural network architecture influences the degree of time reversal symmetry breaking.