ArticleJournal of mathematical neuroscience2020
Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity.
Article in Journal of mathematical neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Journal of computational neuroscience · 2026Article
- Interactions between long- and short-term synaptic plasticity transform temporal neural representations into spatial.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Third-order entropy for spatiotemporal neural network characterization.Journal of neurophysiology · 2025Article
- Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.Neural computation · 2023Article
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- A numerical population density technique for N-dimensional neuron models.Frontiers in neuroinformatics · 2022Article
- Multimodal parameter spaces of a complex multi-channel neuron model.Frontiers in systems neuroscience · 2022Article
- A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.Neuroinformatics · 2022Article
- Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach.Biological cybernetics · 2021Article
- Linear-nonlinear cascades capture synaptic dynamics.PLoS computational biology · 2021Article
- Exact neural mass model for synaptic-based working memory.PLoS computational biology · 2020Article
- Principles underlying the input-dependent formation and organization of memories.Network neuroscience (Cambridge, Mass.) · 2019Article
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Abstract
Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale models of the brain. Here, we extend a recent theory for mesoscopic population dynamics with static synapses to the case of dynamic synapses exhibiting short-term plasticity (STP). The extended theory offers an approximate mean-field dynamics for the synaptic input currents arising from populations of spiking neurons and synapses undergoing Tsodyks-Markram STP. The approximate mean-field dynamics accounts for both finite number of synapses and correlation between the two synaptic variables of the model (utilization and available resources) and its numerical implementation is simple. Comparisons with Monte Carlo simulations of the microscopic model show that in both feedforward and recurrent networks, the mesoscopic mean-field model accurately reproduces the first- and second-order statistics of the total synaptic input into a postsynaptic neuron and accounts for stochastic switches between Up and Down states and for population spikes. The extended mesoscopic population theory of spiking neural networks with STP may be useful for a systematic reduction of detailed biophysical models of cortical microcircuits to numerically efficient and mathematically tractable mean-field models.
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