ArticleNature communications2019
Inferring and validating mechanistic models of neural microcircuits based on spike-train data.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Observing hidden neuronal states in experiments.PLoS computational biology · 2025Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings.Biological cybernetics · 2024Article
- Uncovering the organization of neural circuits with Generalized Phase Locking Analysis.PLoS computational biology · 2023Article
- Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons.Communications biology · 2023Article
- Inferring the temporal evolution of synaptic weights from dynamic functional connectivity.Brain informatics · 2022Article
- In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task.Nature communications · 2022Article
- Optimal Population Coding for Dynamic Input by Nonequilibrium Networks.Entropy (Basel, Switzerland) · 2022Article
- Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories.Nature communications · 2021Article
- Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data.PLoS computational biology · 2021Article
- Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics.Entropy (Basel, Switzerland) · 2020Review
- 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
- Inference of synaptic connectivity and external variability in neural microcircuits.Journal of computational neuroscience · 2020Article
- Resting-State Neural Firing Rate Is Linked to Cardiac-Cycle Duration in the Human Cingulate and Parahippocampal Cortices.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
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5 authors.
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Abstract
The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity.
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