Modeling neural activity at the ensemble level

TL;DR

This paper demonstrates that neural ensemble activity can be effectively modeled using high-dimensional nonlinear dynamical models, enabling better understanding of brain computations at the ensemble level.

Contribution

It introduces a new ensemble dynamical model, a dimensionality reduction method, and a maximum-likelihood approach for parameter estimation, advancing quantitative neural modeling.

Findings

EDMs accurately approximate simulated neural activity.

Dimensionality reduction facilitates parameter estimation.

Models successfully predict auditory cortex responses.

Abstract

Here we demonstrate that the activity of neural ensembles can be quantitatively modeled. We first show that an ensemble dynamical model (EDM) accurately approximates the distribution of voltages and average firing rate per neuron of a population of simulated integrate-and-fire neurons. EDMs are high-dimensional nonlinear dynamical models. To faciliate the estimation of their parameters we present a dimensionality reduction method and study its performance with simulated data. We then introduce and evaluate a maximum-likelihood method to estimate connectivity parameters in networks of EDMS. Finally, we show that this model an methods accurately approximate the high-gamma power evoked by pure tones in the auditory cortex of rodents. Overall, this article demonstrates that quantitatively modeling brain activity at the ensemble level is indeed possible, and opens the way to understanding the computations performed by neural ensembles, which could revolutionarize our understanding of brain function.