ArticleFrontiers in computational neuroscience2013
Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity.PLoS computational biology · 2026Article
- Article
- Inferring synaptic transmission from the stochastic dynamics of the quantal content: An analytical approach.PLoS computational biology · 2025Article
- Power-law adaptation in the presynaptic vesicle cycle.Communications biology · 2025Article
- Exact Distribution of the Quantal Content in Synaptic Transmission.Physical review letters · 2024Article
- Interactions between circuit architecture and plasticity in a closed-loop cerebellar system.eLife · 2024Article
- Sleep and wake in a model of the thalamocortical system with Martinotti cells.The European journal of neuroscience · 2024Article
- The short-term plasticity of VIP interneurons in motor cortex.Frontiers in synaptic neuroscience · 2024Article
- Efficient sampling-based Bayesian Active Learning for synaptic characterization.PLoS computational biology · 2023Article
- How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output.PLoS computational biology · 2023Article
- Predictable Fluctuations in Excitatory Synaptic Strength Due to Natural Variation in Presynaptic Firing Rate.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Developmental depression-to-facilitation shift controls excitation-inhibition balance.Communications biology · 2022Article
- Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning.PLoS computational biology · 2022Article
- Local connectivity and synaptic dynamics in mouse and human neocortex.Science (New York, N.Y.) · 2022Article
- Inferring stimulation induced short-term synaptic plasticity dynamics using novel dual optimization algorithm.PloS one · 2022Article
- Separable actions of acetylcholine and noradrenaline on neuronal ensemble formation in hippocampal CA3 circuits.PLoS computational biology · 2021Article
- Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.Nature neuroscience · 2021Article
- Linear-nonlinear cascades capture synaptic dynamics.PLoS computational biology · 2021Article
- Training deep neural density estimators to identify mechanistic models of neural dynamics.eLife · 2020Article
- Modeling the Short-Term Dynamics ofThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
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3 authors.
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Abstract
Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.
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