Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles

TL;DR

This paper introduces the eigenangle test, a statistical method for comparing neural network matrices by measuring angles between eigenvectors, enabling analysis of structural similarities in activity and connectivity.

Contribution

The study develops a novel eigenangle test for quantifying similarity between neural activity and connectivity matrices, applicable to various network comparison scenarios.

Findings

The eigenangle test effectively compares correlation and adjacency matrices.

It can detect structural differences in neural network models.

Application to a rewiring experiment demonstrates its utility in assessing connectivity effects.

Abstract

Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The "eigenangle test" quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.