Inference of a mesoscopic population model from population spike trains

TL;DR

This paper introduces a mesoscopic modeling approach to infer neural population dynamics from spike train data, enabling the estimation of single-neuron and connectivity parameters with Bayesian methods, thus bridging the gap between detailed neuron models and population-level data.

Contribution

It presents a novel low-dimensional, mechanistic inference framework for fitting population models to empirical neural data, including parameter estimation and Bayesian analysis.

Findings

Successfully recovered single-neuron and connectivity parameters from simulated data.

Demonstrated the use of MCMC for Bayesian inference of model parameters.

Analyzed the impact of mesoscopic approximation on inference accuracy.

Abstract

To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of activity patterns while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single neuron scale to empirical population data. To close this gap, we propose to fit such data at a meso scale, using a mechanistic but low-dimensional and hence statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous `pools' of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived, and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent to a mesoscopic population model impact the accuracy of the inferred single-neuron parameters.