RT Journal Article
SR Electronic
T1 Identification of Animal Behavioral Strategies by Inverse Reinforcement Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 129007
DO 10.1101/129007
A1 Shoichiro Yamaguchi
A1 Honda Naoki
A1 Muneki Ikeda
A1 Yuki Tsukada
A1 Shunji Nakano
A1 Ikue Mori
A1 Shin Ishii
YR 2017
UL http://biorxiv.org/content/early/2017/07/05/129007.abstract
AB Animals are able to flexibly adapt to new environments by controlling different behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals’ decision-making, but methods available 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. As a particular target, we applied this framework to thermotactic behavior in C. elegans. We found that the identified strategy comprised two different modes. The strategy also clarified how the worms control thermosensory states throughout migration, in terms of control theory. Furthermore, we applied our method to thermosensory neuron-deficient worms, to identify the neural basis underlying these strategies. Thus, this study validates and presents a novel approach that should propel the development of new, more effective experiments to identify behavioral strategies and decision-making in animals.