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

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 recordi

SUBMITTER: Ladenbauer J 

PROVIDER: S-EPMC6821748 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature