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Soltani and Wang propose an adaptive model of Bayesian inference with binary cues.

In their model, a synaptic weight codes for the ratio of synapses in a set which are activated vs. de-activated by the binary cue encoded in their pre-synaptic axon's activity.

The stochastic Hebbian learning rule makes the synaptic weights correctly encode log posterior probabilities and the neurons will encode reward probability correctly.