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This study explores Bayesian message passing schemes for neuronal computation. Marginal message passing offers a balance between simplicity and performance for brain-inspired AI.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Bayesian inference

Background:

  • Neuronal computations depend on local synaptic interactions and message propagation within networks.
  • Bayesian message passing schemes are popular for network inference, but their biological plausibility requires examination.

Purpose of the Study:

  • To review and compare variational message passing and belief propagation for neuronal inference.
  • To introduce marginal message passing as a compromise between architectural simplicity and inferential performance.
  • To explore the neuronal manifestation and simulation of these message passing schemes.

Main Methods:

  • Review of variational message passing and belief propagation derived from free energy functionals.
  • Illustration of message passing using Hidden Markov Models and factor graphs.
  • Neuronal simulation of inference processes and comparison of scheme performance.

Main Results:

  • Variational message passing is neuronally plausible but less performant than belief propagation.
  • Belief propagation offers exact marginal posterior computation but lacks architectural simplicity.
  • Marginal message passing provides a simpler architecture while approximating belief propagation's performance.

Conclusions:

  • Marginal message passing presents a viable compromise for neuronal inference architectures.
  • Aberrant message passing may underlie neurological and psychiatric syndromes.
  • The study links formal message passing considerations to brain function and dysfunction.