On why a few points suffice to describe spatiotemporal large-scale brain dynamics

TL;DR

This paper investigates why sparse spatiotemporal point processes effectively compress brain signals, revealing that signals with long-range correlations, common in many natural phenomena, are particularly suitable for this method.

Contribution

It identifies the correlation properties that enable effective compression of brain signals using sparse point processes, explaining the underlying reasons for the approach's success.

Findings

Long-range correlations in signals facilitate effective compression.

Inflection points contain most of the information in correlated signals.

The method is applicable to various natural signals with similar correlation properties.

Abstract

An heuristic signal processing scheme recently introduced shows how brain signals can be efficiently represented by a sparse spatiotemporal point process. The approach has been validated already for different relevant conditions demonstrating that preserves and compress a surprisingly large fraction of the signal information. In this paper the conditions for such compression to succeed are investigated as well as the underlying reasons for such good performance. The results show that the key lies in the correlation properties of the time series under consideration. It is found that signals with long range correlations are particularly suitable for this type of compression, where inflection points contain most of the information. Since this type of correlation is ubiquitous in signals trough out nature including music, weather patterns, biological signals, etc., we expect that this type of approach to be an useful tool for their analysis.