ArticleNature neuroscience2021
Synaptic plasticity as Bayesian inference.
Article in Nature neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- An experimentally informed computational model of neurovestibular adaptation to altered gravity.Experimental physiology · 2026Article
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- Bayesian continual learning and forgetting in neural networks.Nature communications · 2025Article
- Uncertainty-Gated Min-Cost Flows forbioRxiv : the preprint server for biology · 2025Article
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- A unified framework to model synaptic dynamics during the sleep-wake cycle.PLoS biology · 2025Article
- Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.PLoS computational biology · 2024Article
- Article
- Conductance-based dendrites perform Bayes-optimal cue integration.PLoS computational biology · 2024Article
- Presynaptic nanoscale components of retrograde synaptic signaling.Science advances · 2024Article
- Synaptic weights that correlate with presynaptic selectivity increase decoding performance.PLoS computational biology · 2023Article
- The Computational and Neural Bases of Context-Dependent Learning.Annual review of neuroscience · 2023Review
- Post-injury pain and behaviour: a control theory perspective.Nature reviews. Neuroscience · 2023Review
- Neural learning rules for generating flexible predictions and computing the successor representation.eLife · 2023Article
- Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning.PLoS computational biology · 2022Article
- Drifting neuronal representations: Bug or feature?Biological cybernetics · 2022Review
- Prediction Error Determines Whether NMDA Receptors in the Basolateral Amygdala Complex Are Involved in Pavlovian Fear Conditioning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Dendritic Excitability and Synaptic Plasticity In Vitro and In Vivo.Neuroscience · 2022Review
- Natural-gradient learning for spiking neurons.eLife · 2022Article
- Learning as filtering: Implications for spike-based plasticity.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.