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A neural basis of probabilistic computation in visual cortex.

Edgar Y Walker1,2, R James Cotton3,4,5, Wei Ji Ma6

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Summary

This study reveals how the brain encodes uncertainty using neural population activity. Researchers found that these neural representations of uncertainty, specifically likelihood functions, better predict decisions than simple orientation estimates.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Bayesian models suggest organisms represent sensory uncertainty.
  • The neural mechanisms for encoding uncertainty are not well understood.
  • A key hypothesis posits uncertainty is encoded in cortical neuron population activity as likelihood functions.

Purpose of the Study:

  • To investigate if population activity in the visual cortex encodes likelihood functions representing uncertainty.
  • To determine if these encoded likelihood functions influence behavioral decisions.

Main Methods:

  • Simultaneous recording of neural population activity from primate visual cortex during a visual categorization task.
  • Decoding the likelihood function from trial-to-trial neural population activity.
  • Comparing the predictive power of the decoded likelihood function versus a point estimate of stimulus orientation on decisions.

Main Results:

  • The decoded likelihood function significantly predicted behavioral decisions.
  • The likelihood function's predictive power surpassed that of a simple point estimate of orientation.
  • This predictive relationship held even when controlling for the true stimulus orientation, indicating internal neural fluctuations drive behavioral uncertainty.

Conclusions:

  • Population-encoded likelihood functions play a crucial role in mediating behavior.
  • This provides a neural basis for Bayesian models of perception and decision-making.
  • Internal neural dynamics contribute to behaviorally relevant uncertainty representation.