ArticlePLoS computational biology2015
Network Plasticity as Bayesian Inference.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Learning reorganizes dendritic and stabilizes axon initial segment inhibitory synapses in CA1 pyramidal neurons.Nature communications · 2026Article
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Stability through plasticity: Finding robust memories through representational drift.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Distributed and drifting signals for working memory load in human cortex.bioRxiv : the preprint server for biology · 2025Article
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage.Brain sciences · 2025Article
- Modelling neural probabilistic computation using vector symbolic architectures.Cognitive neurodynamics · 2024Article
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- A stable sensory map emerges from a dynamic equilibrium of neurons with unstable tuning properties.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2023Article
- Calibrating Vision: Concepts and Questions.Vision research · 2022Review
- The geometry of representational drift in natural and artificial neural networks.PLoS computational biology · 2022Article
- Longitudinal monitoring of prefrontal cortical ensemble dynamics reveals new insights into stress habituation.Neurobiology of stress · 2022Article
- Drifting neuronal representations: Bug or feature?Biological cybernetics · 2022Review
- Natural gradient enables fast sampling in spiking neural networks.Advances in neural information processing systems · 2022Article
- Bayesian continual learningFrontiers in computational neuroscience · 2022Article
- Article
- Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma.eLife · 2021Article
- Spine dynamics in the brain, mental disorders and artificial neural networks.Nature reviews. Neuroscience · 2021Review
- The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning.Behavior research methods · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.
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