ArticleeNeuro
Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- A Synchronization-Driven Learning Rule for Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.Research square · 2026Article
- Stochasticity in action potential backpropagation: consequences for neuronal computation.Frontiers in cellular neuroscience · 2026Review
- Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Long- and short-term history effects in a spiking network model of statistical learning.Scientific reports · 2023Article
- Post-injury pain and behaviour: a control theory perspective.Nature reviews. Neuroscience · 2023Review
- Adaptive Extreme Edge Computing for Wearable Devices.Frontiers in neuroscience · 2021Review
- Mind the Noise When Identifying Computational Models of Cognition from Brain Activity.Frontiers in neuroscience · 2016Article
- Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.Frontiers in neuroscience · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.
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What OpenQuestion holds
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.