Inferring and validating mechanistic models of neural microcircuits based on spike-train data

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dc.contributor.authorLadenbauer, Josefen
dc.contributor.authorMcKenzie, Samen
dc.contributor.authorEnglish, Daniel Fineen
dc.contributor.authorHagens, Olivieren
dc.contributor.authorOstojic, Srdjanen
dc.contributor.departmentSchool of Neuroscienceen
dc.date.accessioned2018-11-19T18:32:14Zen
dc.date.available2018-11-19T18:32:14Zen
dc.date.issued2018-02-07en
dc.description.abstractThe 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 the maximal-likelihood approach, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Evaluations based on simulated data, and validations using ground truth recordings in vitro and in vivo demonstrated that parameter estimation is very accurate, even for highly sub-sampled networks. We finally apply our methods to recordings from cortical neurons of awake ferrets and reveal population-level equalization between hidden excitatory and inhibitory inputs. The methods introduced here enable a quantitative, mechanistic interpretation of recorded neuronal population activity.en
dc.description.sponsorshipThis work was supported by Deutsche Forschungsgemeinschaft in the framework of Collaborative Research Center 910, the Programme Emergences of the City of Paris, ANR project MORSE (ANR-16-CE37-0016), and the program \Investissements d'Avenir" launched by the French Government and implemented by the ANR, with the references ANR-10-LABX-0087 IEC and ANR-11-IDEX-0001-02 PSL* Research University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.description.versionSubmitted versionen
dc.identifier.doihttps://doi.org/10.1101/261016en
dc.identifier.urihttp://hdl.handle.net/10919/85904en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.titleInferring and validating mechanistic models of neural microcircuits based on spike-train dataen
dc.typeArticleen
dc.type.dcmitypetexten

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Destination Area: Adaptive Brain and Behavior (ABB)
Scholarly Works, School of Neuroscience