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With or without you: predictive coding and Bayesian inference in the brain.

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This study unifies predictive coding and Bayesian inference into a Bayesian predictive coding framework. It clarifies their distinctions and evaluates supporting evidence for neural population coding and dynamics.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Neural population coding and dynamics are explained by two emerging theories: predictive coding and Bayesian inference.
  • These theories share computational concepts and address overlapping empirical phenomena, necessitating clarification.

Purpose of the Study:

  • To describe predictive coding and Bayesian inference.
  • To combine them into a unified Bayesian predictive coding framework.
  • To distinguish between the two theories and evaluate their experimental support.

Main Methods:

  • Theoretical description of predictive coding and Bayesian inference.
  • Conceptual integration into a Bayesian predictive coding framework.
  • Critical evaluation of existing experimental evidence.

Main Results:

  • Predictive coding is identified as an algorithmic motif serving various computational goals, including Bayesian inference.
  • Bayesian inference can employ predictive coding but is not limited to it.
  • The framework of Bayesian predictive coding is presented and its supporting evidence assessed.

Conclusions:

  • Bayesian predictive coding offers a unified functional explanation for neural population coding and dynamics.
  • Distinguishing predictive coding and Bayesian inference is crucial for understanding their specific roles.
  • Further direct experimental testing is needed to validate Bayesian predictive coding.