ArticleeNeuro
Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Inferring synaptic transmission from the stochastic dynamics of the quantal content: An analytical approach.PLoS computational biology · 2025Article
- Exact Distribution of the Quantal Content in Synaptic Transmission.Physical review letters · 2024Article
- Estimation of cumulative amplitude distributions of miniature postsynaptic currents allows characterising their multimodality, quantal size and variability.Scientific reports · 2023Article
- Efficient sampling-based Bayesian Active Learning for synaptic characterization.PLoS computational biology · 2023Article
- Local connectivity and synaptic dynamics in mouse and human neocortex.Science (New York, N.Y.) · 2022Article
- Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation.eLife · 2022Article
- Linear-nonlinear cascades capture synaptic dynamics.PLoS computational biology · 2021Article
- Modeling the Short-Term Dynamics ofThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Identifiability of a Binomial Synapse.Frontiers in computational neuroscience · 2020Article
- Model-Based Inference of Synaptic Transmission.Frontiers in synaptic neuroscience · 2019Review
- Parsing Out the Variability of Transmission at Central Synapses Using Optical Quantal Analysis.Frontiers in synaptic neuroscience · 2019Article
- Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.PLoS computational biology · 2017Article
- The readily releasable pool of synaptic vesicles.Current opinion in neurobiology · 2017Review
- Functional consequences of pre- and postsynaptic expression of synaptic plasticity.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release.Frontiers in computational neuroscience · 2016Article
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Authors and funding
4 authors.
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Abstract
The dependence of the synaptic responses on the history of activation and their large variability are both distinctive features of repetitive transmission at chemical synapses. Quantitative investigations have mostly focused on trial-averaged responses to characterize dynamic aspects of the transmission--thus disregarding variability--or on the fluctuations of the responses in steady conditions to characterize variability--thus disregarding dynamics. We present a statistically principled framework to quantify the dynamics of the probability distribution of synaptic responses under arbitrary patterns of activation. This is achieved by constructing a generative model of repetitive transmission, which includes an explicit description of the sources of stochasticity present in the process. The underlying parameters are then selected via an expectation-maximization algorithm that is exact for a large class of models of synaptic transmission, so as to maximize the likelihood of the observed responses. The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.
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