Bayesian topology identification of linear dynamic networks

In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the connectivity of networks of transfer functions, without the need to estimate the dynamics. The algorithm employs a Bayesian measure and a forward-backward search algorithm. To obtain the Bayesian measure, the impulse responses of network modules are modeled as Gaussian processes and the hyperparameters are estimated by marginal likelihood maximization using the expectation-maximization algorithm. Numerical results demonstrate the effectiveness of this method

Informative and misinformative interactions in a school of fish

It is generally accepted that, when moving in groups, animals process information to coordinate their motion. Recent studies have begun to apply rigorous methods based on Information Theory to quantify such distributed computation. Following this perspective, we use transfer entropy to quantify dynamic information flows locally in space and time across a school of fish during directional changes around a circular tank, i.e. U-turns. This analysis reveals peaks in information flows during collective U-turns and identifies two different flows: an informative flow (positive transfer entropy) based on fish that have already turned about fish that are turning, and a misinformative flow (negative transfer entropy) based on fish that have not turned yet about fish that are turning. We also reveal that the information flows are related to relative position and alignment between fish, and identify spatial patterns of information and misinformation cascades. This study offers several methodological contributions and we expect further application of these methodologies to reveal intricacies of self-organisation in other animal groups and active matter in general

Quantifying criticality, information dynamics and thermodynamics of collective motion

Active matter consists of self-propelled particles whose interactions give rise to coherent collective motion. Well-known examples include schools of fish, flocks of birds, swarms of insects and herds of ungulates. On the micro-scale, cells, enzymes and bacteria also move collectively as active matter, inspiring engineering of artificial materials and devices. These diverse systems exhibit similar collective behaviours, including gathering, alignment and quick propagation of perturbations, which emerge from relatively simple local interactions. This phenomenon is known as self-organisation and is observed in active matter as well as in many other complex collective phenomena, including urban agglomeration, financial crises, ecosystems dynamics and technological cascading failures. Some open challenges in the study of self-organisation include (a) how the information processing across the collective and over time gives rise to emergent behaviour, (b) how to identify the regimes in which different collective behaviours exist and their phase transitions, and (c) how to quantify the thermodynamics associated with these phenomena. This thesis aims to investigate these topics in the context of active matter, while building a rigorous theoretical framework. Specifically, this thesis provides three main contributions. Firstly, the question of how to formally measure information transfer across the collective is addressed and applied to a real system, i.e., a school of fish. Secondly, general relations between statistical mechanical and thermodynamical quantities are analytically derived and applied to a model of active matter, resulting in the formulation of the concept of “thermodynamic efficiency of computation during collective motion”. This concept is then extended to the domain of urban dynamics. Thirdly, this thesis provides a rigorous quantification of the non-equilibrium entropy production associated with the collective motion of active Brownian particles

Inferring causal relationships in zebrafish-robot interactions through transfer entropy: a small lure to catch a big fish

In the field of animal behavior, effective methods to apprehend causal relationships that underlie the interactions between animals are in dire need. How to identify a leader in a group of social animals or quantify the mutual response of predator and prey are exemplary questions that would benefit from an improved understanding of causality. Information theory offers a potent framework to objectively infer cause-and-effect relationships from raw experimental data, in the form of behavioral observations or individual trajectory tracks. In this targeted review, we summarize recent advances in the application of the information-theoretic concept of transfer entropy to animal interactions. First, we offer an introduction to the theory of transfer entropy, keeping a balance between fundamentals and practical implementation. Then, we focus on animal-robot experiments as a means for the validation of the use of transfer entropy to measure causal relationships. We explore a test battery of robotics-based protocols designed for studying zebrafish social behavior and fear response. Grounded in experimental evidence, we demonstrate the potential of transfer entropy to assist in the detection and quantification of causal relationships in animal interactions. The proposed robotics-based platforms offer versatile, controllable, and customizable stimuli to generate a priori known cause-and-effect relationships, which would not be feasible with live stimuli. We conclude the paper with an outlook on possible applications of transfer entropy to study group behavior and clarify the determinants of leadership in social animals

Collective behaviour: Investigating the underlying mechanisms, development, and function

The aim of this thesis was to contribute to our understanding of collective behaviour by capitalising upon recent advances in tracking software and analytical techniques. Using Tinbergen’s 4 questions as a framework, this thesis investigated the mechanisms, development and function of collective behaviour. In Chapters 2 and 3, I suggest that the ability to coordinate group movement is conserved across different developmental contexts and that speed can govern certain group-level patterns in a similar way across multiple species. In chapter 3, I found that information flow increased with speed and alignment, providing support for the idea that coordinated collective movement might have an adaptive value by allowing animal groups to respond rapidly and in a synchronised manner to changes in their environment. In Chapters 4 and 5, I used different socially living species to investigate the adaptive value of collective behaviour, providing evidence that group-living species can assess and respond to risk in a nuanced manner. In a coral reef system, I discovered humbug damselfish have the ability to selectively integrate cues from their abiotic and biotic environment. In a freshwater system, I discovered that mosquitofish have the ability to alter behaviour based on slight changes in predator behaviour and angular position