Use this URL to cite or link to this record in EThOS:
Title: Data-driven modelling of zebrafish locomotion and collective behaviour
Author: Zienkiewicz, Adam Kasimir
ISNI:       0000 0004 6057 1198
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
The rich spatio-temporal patterns of animal collective motion provide a fascinating example of emergent phenomena in complex dynamical systems. Spanning orders of magnitude in scale, similar collective behaviours are observed in fish shoals, bird flocks, insect colonies and microscopic bacterial clusters. In each example, the propagation of localised interactions between many individuals, each responding to environmental and social cues, leads to long-ranged order and persistent collective states. Crucially, the perpetual exchange of shared information between individuals facilitates both distributed sensing and collective decision-making, conferring numerous benefits to the group as a whole. In this study, we describe the dynamical behaviour of zebrafish , an important model species for which suitable models are currently unavailable. The emergence and properties of collective motion, particularly within fish shoals, have been studied extensively; described as systems of interacting self-propelled particles. Heuristic models implementing a variety of behavioural rules, have revealed a limited, universal set of collective states. Here, we adopt a bottom-up approach based directly on experimental observations in order to obtain a deeper understanding of the diverse mechanisms which underpin zebrafish collective behaviour. In this study we successfully unite two recent methodologies; employing a data-driven stochastic modelling framework to capture individual locomotory signatures, in which the structure and dynamics of interactions are informed by mapping 'social forces' between co-swimming zebrafish pairs. From our analysis of experimental data, we find that zebrafish locomotion and their collective behaviour demands a model in which dynamic speed-regulation and alignment responses are explicitly prescribed. In subsequent numerical experiments, we demonstrate the additional role of speedregulation for leadership within fish shoals and suggest strategies to control the collective dynamics of large shoals via the influence of a small subset of informed individuals. During the course of this investigation, we also highlight the importance of social-networks in collective behaviour and outline a novel method for computing the visual fields of fish within a shoal.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available