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

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. On the other hand, mechanistic neuronal models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. 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. Parameter estimation is found to be very accurate, even for highly sub-sampled networks. We apply our methods to recordings from cortical neurons of awake ferrets to reveal properties of the hidden synaptic inputs, in particular underlying population-level equalization between excitatory and inhibitory inputs. The methods introduced here open the door to a quantitative, mechanistic interpretation of recorded neuronal population activity.