Correlation functions as a tool to study collective behaviour phenomena in biological systems

TL;DR

This paper reviews how two-point correlation functions can be used to analyze collective behaviors in biological systems, providing practical methods and applying them to neuronal models.

Contribution

It offers a self-contained guide for calculating correlation functions from data and discusses their properties in biological and critical systems.

Findings

Correlation functions reveal collective behavior in biological systems.

Finite system size affects correlation measurements.

Application to neuronal models demonstrates practical utility.

Abstract

Much of interesting complex biological behaviour arises from collective properties. Important information about collective behaviour lies in the time and space structure of fluctuations around average properties, and two-point correlation functions are a fundamental tool to study these fluctuations. We give a self-contained presentation of definitions and techniques for computation of correlation functions aimed at providing students and researchers outside the field of statistical physics a practical guide to calculating correlation functions from experimental and simulation data. We discuss some properties of correlations in critical systems, and the effect of finite system size, which is particularly relevant for most biological experimental systems. Finally we apply these to the case of the dynamical transition in a simple neuronal model,