TY - JOUR
T1 - Analysis of Pairwise Interactions in a Maximum Likelihood Sense to Identify Leaders in a Group
AU - Mwaffo, Violet
AU - Butail, Sachit
AU - Porfiri, Maurizio
N1 - Funding Information:
This work was supported by the National Science Foundation under Grant numbers # CMMI-1433670 and # CMMI-1505832, the Mitsui USA Foundation, and the Army Research Office under Grant number # W911NF-15-1-0267, with Drs. Samuel C. Stanton and Alfredo Garcia as the program managers.
Publisher Copyright:
© Copyright © 2017 Mwaffo, Butail and Porfiri.
PY - 2017/7/31
Y1 - 2017/7/31
N2 - Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader–follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of self-propelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data but it also allows for exploring the role of psychoactive compounds on leader–follower relationships.
AB - Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader–follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of self-propelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data but it also allows for exploring the role of psychoactive compounds on leader–follower relationships.
KW - ROC
KW - classification
KW - event synchronization
KW - network
KW - self-propelled particles
KW - transfer entropy
KW - zebrafish
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U2 - 10.3389/frobt.2017.00035
DO - 10.3389/frobt.2017.00035
M3 - Article
AN - SCOPUS:85029717343
SN - 2296-9144
VL - 4
JO - Frontiers in Robotics and AI
JF - Frontiers in Robotics and AI
M1 - 35
ER -