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Higher-level spatial prediction in natural vision across mouse visual cortex.

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Sensory cortex predicts incoming signals, with more predictable natural scenes evoking weaker brain responses. This predictive ability, especially for higher-level features, operates independently of recent experience.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Predictive processing theories suggest sensory systems anticipate incoming signals based on context.
  • Existing evidence for prediction in sensory cortex often relies on artificial stimuli, not natural perception.

Purpose of the Study:

  • To investigate sensory prediction during natural scene perception in the mouse visual cortex.
  • To quantify spatial predictability in natural images and correlate it with neural responses.

Main Methods:

  • Utilized deep generative modeling to assess spatial predictability of image patches.
  • Analyzed large-scale, high-density recordings from the Allen Institute Brain Observatory.
  • Controlled for tuning to low-level image features and local statistical context.

Main Results:

  • Cortical responses are modulated by sensory predictability; predictable patches elicit weaker responses.
  • Visual cortical neurons show sensitivity to higher-level feature predictability, even in primary visual areas.
  • Unpredictability sensitivity is more pronounced in superficial layers of the primary visual cortex.
  • Spatial prediction effects are independent of recent experience, suggesting reliance on long-term priors.

Conclusions:

  • The visual cortex predominantly predicts sensory information at higher levels of abstraction.
  • Findings align with predictive coding models and self-supervised learning in artificial intelligence.