ArticlePLoS computational biology2021
Linear-nonlinear cascades capture synaptic dynamics.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- A computational theory of short-term synaptic plasticity: synapses learn to tell time.Research square · 2026Article
- Nonlinear recurrent inhibition through facilitating serotonin release in the raphe.Nature neuroscience · 2025Article
- Power-law adaptation in the presynaptic vesicle cycle.Communications biology · 2025Article
- Grid cells, border cells, and discrete complex analysis.Frontiers in computational neuroscience · 2023Article
- Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex predicted by deep learning of experimental recordings.Communications biology · 2022Article
- Optimizing interneuron circuits for compartment-specific feedback inhibition.PLoS computational biology · 2022Article
- Inferring stimulation induced short-term synaptic plasticity dynamics using novel dual optimization algorithm.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.
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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.