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.⇒