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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 functionaldoi:10.1038/s41598-018-38246-3 pmid:30760782 pmcid:PMC6374414 fatcat:3u6w7kywufdw7hgqck5lalmhf4