TY - JOUR
T1 - Model-free information-theoretic approach to infer leadership in pairs of zebrafish
AU - Butail, Sachit
AU - Mwaffo, Violet
AU - Porfiri, Maurizio
N1 - Funding Information:
This work was supported by the National Science Foundation under Grants No. CMMI-1433670 and No. CMMI-1505832.
Publisher Copyright:
©2016 American Physical Society.
PY - 2016/4/18
Y1 - 2016/4/18
N2 - Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.
AB - Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.
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U2 - 10.1103/PhysRevE.93.042411
DO - 10.1103/PhysRevE.93.042411
M3 - Article
C2 - 27176333
AN - SCOPUS:84964432734
VL - 93
JO - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
JF - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
SN - 1063-651X
IS - 4
M1 - 042411
ER -