ArticleScientific reports2019
Neuronal message passing using Mean-field, Bethe, and Marginal approximations.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
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- Freud's Model of the Mind Within a Predictive Processing Neuroscientific Paradigm.Entropy (Basel, Switzerland) · 2026Article
- The active construction of past episodes.Translational neuroscience · 2026Article
- SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025Review
- The Physics and Metaphysics of Social Powers: Bridging Cognitive Processing and Social Dynamics, a New Perspective on Power Through Active Inference.Entropy (Basel, Switzerland) · 2025Article
- Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging.Brain sciences · 2024Review
- Generative models for sequential dynamics in active inference.Cognitive neurodynamics · 2024Review
- Cognitive Graphs: Representational Substrates for Planning.Decision (Washington, D.C.) · 2024Article
- Space is a latent sequence: A theory of the hippocampus.Science advances · 2024Article
- Joint modeling of choices and reaction times based on Bayesian contextual behavioral control.PLoS computational biology · 2024Article
- A social inference model of idealization and devaluation.Psychological review · 2024Article
- Federated inference and belief sharing.Neuroscience and biobehavioral reviews · 2024Review
- A primer on Variational Laplace (VL).NeuroImage · 2023Article
- Reward Maximization Through Discrete Active Inference.Neural computation · 2023Article
- Active Inference, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia.Schizophrenia bulletin · 2023Article
- Active inference and the two-step task.Scientific reports · 2022Article
- In the Body's Eye: The computational anatomy of interoceptive inference.PLoS computational biology · 2022Article
- A step-by-step tutorial on active inference and its application to empirical data.Journal of mathematical psychology · 2022Article
- How particular is the physics of the free energy principle?Physics of life reviews · 2022Review
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4 authors.
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Abstract
Neuronal computations rely upon local interactions across synapses. For a neuronal network to perform inference, it must integrate information from locally computed messages that are propagated among elements of that network. We review the form of two popular (Bayesian) message passing schemes and consider their plausibility as descriptions of inference in biological networks. These are variational message passing and belief propagation - each of which is derived from a free energy functional that relies upon different approximations (mean-field and Bethe respectively). We begin with an overview of these schemes and illustrate the form of the messages required to perform inference using Hidden Markov Models as generative models. Throughout, we use factor graphs to show the form of the generative models and of the messages they entail. We consider how these messages might manifest neuronally and simulate the inferences they perform. While variational message passing offers a simple and neuronally plausible architecture, it falls short of the inferential performance of belief propagation. In contrast, belief propagation allows exact computation of marginal posteriors at the expense of the architectural simplicity of variational message passing. As a compromise between these two extremes, we offer a third approach - marginal message passing - that features a simple architecture, while approximating the performance of belief propagation. Finally, we link formal considerations to accounts of neurological and psychiatric syndromes in terms of aberrant message passing.
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