Data-driven stochastic modelling of zebrafish locomotion.

ABSTRACT: In this work, we develop a data-driven modelling framework to reproduce the locomotion of fish in a confined environment. Specifically, we highlight the primary characteristics of the motion of individual zebrafish (Danio rerio), and study how these can be suitably encapsulated within a mathematical framework utilising a limited number of calibrated model parameters. Using data captured from individual zebrafish via automated visual tracking, we develop a model using stochastic differential equations and describe fish as a self propelled particle moving in a plane. Based on recent experimental evidence of the importance of speed regulation in social behaviour, we extend stochastic models of fish locomotion by introducing experimentally-derived processes describing dynamic speed regulation.

SUBMITTER: Zienkiewicz A 

PROVIDER: S-EPMC4598355 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature