Statistical modelling of higher-order correlations in pools of neural activity

TL;DR

This paper introduces a statistical model based on information geometry to quantify high-order correlations in large neural ensembles, overcoming combinatorial complexity and sampling issues.

Contribution

It proposes a reduced-parameter model using a deformation parameter within the information geometry framework to characterize high-order neural correlations.

Findings

Quantifies high-order correlations in large neural pools.

Reduces parameter complexity using a deformation parameter.

Provides insights into neural network dynamics.

Abstract

Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a $(2^N-1)$-dimensional model for a population with $N$ neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from high-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of high-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.