Skip to content

Data-driven modelling of social forces and collective behaviour in zebrafish

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)39-51
Number of pages13
JournalJournal of Theoretical Biology
Volume443
Early online date31 Jan 2018
DOIs
DateAccepted/In press - 12 Jan 2018
DateE-pub ahead of print - 31 Jan 2018
DatePublished (current) - 14 Apr 2018

Abstract

Zebrafish are rapidly emerging as a powerful model organism in hypothesis-driven studies targeting a number of functional and dysfunctional processes. Mathematical models of zebrafish behaviour can inform the design of experiments, through the unprecedented ability to perform pilot trials on a computer. At the same time, in-silico experiments could help refining the analysis of real data, by enabling the systematic investigation of key neurobehavioural factors. Here, we establish a data-driven model of zebrafish social interaction. Specifically, we derive a set of interaction rules to capture the primary response mechanisms which have been observed experimentally. Contrary to previous studies, we include dynamic speed regulation in addition to turning responses, which together provide attractive, repulsive and alignment interactions between individuals. The resulting multi-agent model provides a novel, bottom-up framework to describe both the spontaneous motion and individual-level interaction dynamics of zebrafish, inferred directly from experimental observations.

    Research areas

  • Zebrafish, Stochastic differential equations, Agent-based modelling, Data-driven

Download statistics

No data available

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0022519318300195 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 15.4 MB, PDF document

    Licence: CC BY-NC-ND

DOI

View research connections

Related faculties, schools or groups