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Here we demonstrate that the activity of neural ensembles can bequantitatively modeled. We first show that an ensemble dynamical model (EDM)accurately approximates the distribution of voltages and average firing rateper neuron of a population of simulated integrate-and-fire neurons. EDMs arehigh-dimensional nonlinear dynamical models. To faciliate the estimation oftheir parameters we present a dimensionality reduction method and study itsperformance with simulated data. We then introduce and evaluate amaximum-likelihood method to estimate connectivity parameters in networks ofEDMS. Finally, we show that this model an methods accurately approximate thehigh-gamma power evoked by pure tones in the auditory cortex of rodents.Overall, this article demonstrates that quantitatively modeling brain activityat the ensemble level is indeed possible, and opens the way to understandingthe computations performed by neural ensembles, which could revolutionarize ourunderstanding of brain function.

Modeling neural activity at the ensemble level