Connecting the dots in ethology: applying network theory to understand neural and animal collectives. (April 2022)

• Record Type: Journal Article
• Authors: Gosztolai, Adam; Ramdya, Pavan
• Is Part Of: Current opinion in neurobiology. Volume 73(2022)
• Publication Date: 2022-04
• Subjects: Network theory -- Neural circuits -- Behavior -- Collective behavior -- Functional recordings -- Connectomics -- Tracking -- Network topology -- Controllability -- Symmetry -- Geometry; Neurobiology -- Periodicals; 573.8
• DOI: 10.1016/j.conb.2022.102532
• Languages: English
• ISSNs: 0959-4388

Abstract

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. Highlights: Large-scale neural and behavioral datasets enable a network-based understanding of individual and collective animal behavior. Neuronal and animal interaction networks are interleaved computational layers giving rise to information processing and behavior. Both neural activity and animal behavior can be represented as dynamic signals over networks. Network theory concepts like signal propagation, controllability, symmetry, and geometry uncover structure–function relationships.