Synaptic 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....
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Zusammenfassung: | 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 PSP 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 post-synaptic potentials,
and make falsifiable experimental predictions. |
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DOI: | 10.48550/arxiv.1410.1029 |