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

Josef Ladenbauer (), Sam McKenzie, Daniel Fine English, Olivier Hagens and Srdjan Ostojic ()
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Josef Ladenbauer: Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, École Normale Supérieure, PSL Research University
Sam McKenzie: Neuroscience Institute, New York University
Daniel Fine English: School of Neuroscience, Virginia Tech
Olivier Hagens: Laboratory of Neural Microcircuitry, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne
Srdjan Ostojic: Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, École Normale Supérieure, PSL Research University

Nature Communications, 2019, vol. 10, issue 1, 1-17

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12572-0

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