biostudies-literatureAitchison LSwiss National Science FoundationSimons Collaboration for the Global BrainUCL Graduate Research and UCL Overseas Research ScholarshipsWellcome TrustGatsby Charitable Foundation565-571https://www.ebi.ac.uk/biostudies/studies/S-EPMC7617048biostudies-literatureUnknown24(4)Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights is the optimal strategy. Here we hypothesize that synapses take that strategy; in essence, when they estimate weights, they include error bars. They then use that uncertainty to adjust their learning rates, with more uncertain weights having higher learning rates. We also make a second, independent, hypothesis: synapses communicate their uncertainty by linking it to variability in postsynaptic potential size, with more uncertainty leading to more variability. These two hypotheses cast synaptic plasticity as a problem of Bayesian inference, and thus provide a normative view of learning. They generalize known learning rules, offer an explanation for the large variability in the size of postsynaptic potentials and make falsifiable experimental predictions.Nature neuroscienceSynaptic plasticity as Bayesian inference.PMC7617048150637110114PP00P3 150637110114/Z/15/ZJegminat JPfister JPPouget AAitchison LMenendez JALatham PEfalseSynaptic plasticity as Bayesian inference.Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights is the optimal strategy. Here we hypothesize that synapses take that strategy; in essence, when they estimate weights, they include error bars. They then use that uncertainty to adjust their learning rates, with more uncertain weights having higher learning rates. We also make a second, independent, hypothesis: synapses communicate their uncertainty by linking it to variability in postsynaptic potential size, with more uncertainty leading to more variability. These two hypotheses cast synaptic plasticity as a problem of Bayesian inference, and thus provide a normative view of learning. They generalize known learning rules, offer an explanation for the large variability in the size of postsynaptic potentials and make falsifiable experimental predictions.2021-01-01T00:00:00Z2021 Apr2025-04-19T17:28:05.258Z2025-04-19T17:28:05.258ZS-EPMC76170483370775410.1038/s41593-021-00809-5