Identification of animal behavioral strategies by inverse reinforcement learning.

ABSTRACT: Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animal's behavioral strategy from behavioral time-series data. We applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, fed worms prefer, while starved worms avoid the cultivation temperature on a thermal gradient. Our IRL approach revea

SUBMITTER: Yamaguchi S 

PROVIDER: S-EPMC5951592 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature