Synaptic computation underlying probabilistic inference

People and animals are capable of making decisions using information about the probabilistic associations between a combination of cues and an outcome. Here the authors use computational theory to suggest that the posterior ratio, an important quantity for forming probabilistic inferences, can be learned and encoded by synapses that have bounded weights and undergo reward-dependent Hebbian plasticity.