Connecting the dots in ethology: applying network theory to understand neural and animal collectives

TL;DR

This paper reviews how network theory can be applied to analyze neural and animal collective behaviors, helping to understand the complex interactions that generate population-level actions.

Contribution

It demonstrates the application of network analysis techniques to modern datasets in ethology, providing new insights into the mechanisms of collective behavior.

Findings

Network analysis reveals structure-function relationships in neural and animal collectives.

Analyzing signal propagation and controllability helps understand behavior dynamics.

Network geometry and symmetry are key to deciphering collective system behavior.

Abstract

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure-function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales.