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Predictive Coding, Variational Autoencoders, and Biological Connections.

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This review connects predictive coding in neuroscience and variational autoencoders in machine learning, revealing shared mathematical foundations. These insights bridge the fields and suggest new research directions.

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Area of Science:

  • Neuroscience and Machine Learning

Background:

  • Predictive coding is a prominent theory in theoretical neuroscience.
  • Variational autoencoders are a key area within machine learning.

Purpose of the Study:

  • To identify the common origin and mathematical framework of predictive coding and variational autoencoders.
  • To foster dialogue between neuroscience and machine learning by highlighting shared principles.

Main Methods:

  • Reviewing theoretical neuroscience literature on predictive coding.
  • Analyzing machine learning literature on variational autoencoders.
  • Comparing the underlying mathematical frameworks of both areas.

Main Results:

  • Predictive coding and variational autoencoders share a common mathematical foundation.
  • Cortical pyramidal dendrites may be analogous to nonlinear deep networks.
  • Lateral inhibition could be analogous to normalizing flows.

Conclusions:

  • Connecting predictive coding and variational autoencoders offers valuable insights for both neuroscience and machine learning.
  • The identified correspondences suggest novel avenues for future research in both disciplines.