Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications

Haruki Oikawa; Yoshito Tsuruda; Yoshitake Sano; Teiichi Furuichi; Masataka Yamamoto; Hiroshi Takemura

doi:10.1109/SMC53992.2023.10394161

Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications

Haruki Oikawa, Yoshito Tsuruda, Yoshitake Sano, Teiichi Furuichi, Masataka Yamamoto, Hiroshi Takemura

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In neuroscience research, in vivo imaging techniques for mice are used to observe brain activity and link it to their behavior. Brain activity can often only be associated with observed behavioral outcomes. In other words, it is difficult to speculate on unmanifested behavior due to factors such as 'hesitation' in humans. When a prediction model can predict mice behavior, if brain activity is observed in a specific brain region during incorrect predictions, that would be strong evidence of unmanifest behavior. In this study, we developed a trajectory prediction model to predict the walking trajectory of mice as a prelude to the behavior prediction model. The prediction model was applied to the behavioral analysis of mice administered an anxiolytic drug (diazepam) or saline, revealing significantly different outcomes.

Original language	English
Title of host publication	2023 IEEE International Conference on Systems, Man, and Cybernetics
Subtitle of host publication	Improving the Quality of Life, SMC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5114-5119
Number of pages	6
ISBN (Electronic)	9798350337020
DOIs	https://doi.org/10.1109/SMC53992.2023.10394161
Publication status	Published - 2023
Event	2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 - Hybrid, Honolulu, United States Duration: 1 Oct 2023 → 4 Oct 2023

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Conference

Conference	2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Country/Territory	United States
City	Hybrid, Honolulu
Period	1/10/23 → 4/10/23

Keywords

Deep-Learning
Neuroscience
RGB-D camera

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10.1109/SMC53992.2023.10394161

Cite this

Oikawa, H., Tsuruda, Y., Sano, Y., Furuichi, T., Yamamoto, M., & Takemura, H. (2023). Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications. In 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings (pp. 5114-5119). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC53992.2023.10394161

Oikawa, Haruki ; Tsuruda, Yoshito ; Sano, Yoshitake et al. / Deep-Learning Approach for Revealing Latent Behaviors in Mice : Development of Walking Trajectories Prediction Model and Applications. 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 5114-5119 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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title = "Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications",

abstract = "In neuroscience research, in vivo imaging techniques for mice are used to observe brain activity and link it to their behavior. Brain activity can often only be associated with observed behavioral outcomes. In other words, it is difficult to speculate on unmanifested behavior due to factors such as 'hesitation' in humans. When a prediction model can predict mice behavior, if brain activity is observed in a specific brain region during incorrect predictions, that would be strong evidence of unmanifest behavior. In this study, we developed a trajectory prediction model to predict the walking trajectory of mice as a prelude to the behavior prediction model. The prediction model was applied to the behavioral analysis of mice administered an anxiolytic drug (diazepam) or saline, revealing significantly different outcomes.",

keywords = "Deep-Learning, Neuroscience, RGB-D camera",

author = "Haruki Oikawa and Yoshito Tsuruda and Yoshitake Sano and Teiichi Furuichi and Masataka Yamamoto and Hiroshi Takemura",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023 ; Conference date: 01-10-2023 Through 04-10-2023",

year = "2023",

doi = "10.1109/SMC53992.2023.10394161",

language = "English",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5114--5119",

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Oikawa, H, Tsuruda, Y, Sano, Y, Furuichi, T, Yamamoto, M & Takemura, H 2023, Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications. in 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Institute of Electrical and Electronics Engineers Inc., pp. 5114-5119, 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Hybrid, Honolulu, United States, 1/10/23. https://doi.org/10.1109/SMC53992.2023.10394161

Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications. / Oikawa, Haruki; Tsuruda, Yoshito; Sano, Yoshitake et al.
2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 5114-5119 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Deep-Learning Approach for Revealing Latent Behaviors in Mice

T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

AU - Oikawa, Haruki

AU - Tsuruda, Yoshito

AU - Sano, Yoshitake

AU - Furuichi, Teiichi

AU - Yamamoto, Masataka

AU - Takemura, Hiroshi

PY - 2023

Y1 - 2023

N2 - In neuroscience research, in vivo imaging techniques for mice are used to observe brain activity and link it to their behavior. Brain activity can often only be associated with observed behavioral outcomes. In other words, it is difficult to speculate on unmanifested behavior due to factors such as 'hesitation' in humans. When a prediction model can predict mice behavior, if brain activity is observed in a specific brain region during incorrect predictions, that would be strong evidence of unmanifest behavior. In this study, we developed a trajectory prediction model to predict the walking trajectory of mice as a prelude to the behavior prediction model. The prediction model was applied to the behavioral analysis of mice administered an anxiolytic drug (diazepam) or saline, revealing significantly different outcomes.

AB - In neuroscience research, in vivo imaging techniques for mice are used to observe brain activity and link it to their behavior. Brain activity can often only be associated with observed behavioral outcomes. In other words, it is difficult to speculate on unmanifested behavior due to factors such as 'hesitation' in humans. When a prediction model can predict mice behavior, if brain activity is observed in a specific brain region during incorrect predictions, that would be strong evidence of unmanifest behavior. In this study, we developed a trajectory prediction model to predict the walking trajectory of mice as a prelude to the behavior prediction model. The prediction model was applied to the behavioral analysis of mice administered an anxiolytic drug (diazepam) or saline, revealing significantly different outcomes.

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T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

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BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics

PB - Institute of Electrical and Electronics Engineers Inc.

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Oikawa H, Tsuruda Y, Sano Y, Furuichi T, Yamamoto M , Takemura H. Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications. In 2023 IEEE International Conference on Systems, Man, and Cybernetics: Improving the Quality of Life, SMC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 5114-5119. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/SMC53992.2023.10394161

Deep-Learning Approach for Revealing Latent Behaviors in Mice: Development of Walking Trajectories Prediction Model and Applications

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