Probabilistic models of individual and collective animal behavior

Bod'ová, Katarína and Mitchell, Gabriel J and Harpaz, Roy and Schneidman, Elad and Tkačik, Gašper (2018) Probabilistic models of individual and collective animal behavior. PLoS One, 13 (3). Article number: e0193049 . ISSN 1932-6203

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Official URL: http://dx.doi.org/10.1371/journal.pone.0193049

Abstract

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.

Item Type: Article
DOI: 10.1371/journal.pone.0193049
Subjects: 500 Science > 530 Physics
500 Science > 570 Life sciences; biology > 571 Physiology
Research Group: Tkacik Group
SWORD Depositor: Sword Import User
Depositing User: Sword Import User
Date Deposited: 03 Apr 2018 11:45
Last Modified: 03 Apr 2018 11:45
URI: https://repository.ist.ac.at/id/eprint/995

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