Nature Communications
| Autor: | Daniel F. English, Srdjan Ostojic, Sam McKenzie, Olivier Hagens, Josef Ladenbauer |
|---|---|
| Přispěvatelé: | Laboratoire de Neurosciences Cognitives & Computationnelles (LNC2), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), School of Neuroscience |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Patch-Clamp Techniques Computer science Spike train General Physics and Astronomy Action Potentials Quantitative Biology::Cell Behavior fire neurons Mice Computer Science::Emerging Technologies 0302 clinical medicine Dynamical systems lcsh:Science Neural decoding currents Network model Visual Cortex Neurons 0303 health sciences Likelihood Functions Multidisciplinary Estimation theory Pyramidal Cells dynamics connectivity Excitatory postsynaptic potential Spike (software development) [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] Single trial Science Quantitative Biology::Tissues and Organs Inhibitory postsynaptic potential General Biochemistry Genetics and Molecular Biology Synthetic data Article Quantitative Biology::Subcellular Processes 03 medical and health sciences framework Interneurons Animals Computer Simulation Neuronal adaptation 030304 developmental biology Network models Quantitative Biology::Neurons and Cognition business.industry variability Model selection Reproducibility of Results Pattern recognition Statistical model General Chemistry Cortical neurons Rats 030104 developmental biology networks lcsh:Q maximum-likelihood Artificial intelligence business Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Nature Communications Nature Communications, Nature Publishing Group, 2019, 10 (1), ⟨10.1038/s41467-019-12572-0⟩ Nature Communications, Vol 10, Iss 1, Pp 1-17 (2019) |
| ISSN: | 2041-1723 |
| Popis: | 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. It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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