A hybrid versatile method for state estimation and feature extraction from the trajectory of animal behavior
Shuhei J. Yamazaki, Kazuya Ohara, Kentaro Ito, Nobuo Kokubun, Takuma Kitanishi, Daisuke Takaichi, Yasufumi Yamada, Yosuke Ikejiri, Fumie Hiramatsu, Kosuke Fujita, Yuki Tanimoto, Akiko Yamazoe-Umemoto, Koichi Hashimoto, Katsufumi Sato, Ken Yoda, , Koutarou D. Kimura
This paper introduces a hybrid machine learning method to estimate behavioral states and extract features from low-content animal trajectory data, validated across diverse species and environments.
Frontiers in Neuroscience · Jun 28, 2019Peer-reviewedNeuroscience