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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.17 No.3
- 발행연도
- 2023.9
- 수록면
- 100 - 108 (9page)
- DOI
- 10.5626/JCSE.2023.17.3.100
이용수
초록· 키워드
오류제보하기
The Y-maze behavioral test is a pivotal tool for assessing the memory and exploratory tendencies of mice in novel environments. A significant aspect of this test involves the continuous tracking and pinpointing of the mouse’s location, a task that can be labor-intensive for human researchers. This study introduced an automated solution to this challenge through camera-based image processing. We argued that key point localization techniques are more effective than object detection methods, given that only a single mouse is involved in the test. Through an experimental comparison of eight distinct neural network architectures, we identified the most effective structures for localizing key points such as the mouse’s nose, body center, and tail base. Our models were designed to predict not only the mouse key points but also the reference points of the Y-maze device, aiming to streamline the analysis process and minimize human intervention. The approach involves the generation of a heatmap using a deep learning neural network structure, followed by the extraction of the key points’ central location from the heatmap using a soft argmax function. The findings of this study provide a practical guide for experimenters in the selection and application of neural network architectures for Y-maze behavioral testing.
목차
Abstract
I. INTRODUCTION
II. RELATED WORKS
III. HEATMAP REGRESSION
IV. EXPERIMENTS
V. CONCLUSION
REFERENCES
참고문헌 (35)
참고문헌 신청
D. M. Bannerman, B. Niewoehner, L. Lyon, C. Romberg, W. B. Schmitt, A. Taylor, et al., “NMDA receptor subunit NR2A is required for rapidly acquired spatial working memory but not incremental spatial reference memory,” Journal of Neuroscience, vol. 28, no.
Stanford School of Medicine, “Y Maze Spontaneous Alternation Test,” 2023 [Online]. Available: https://med.stanford.edu/sbfnl/services/bm/lm/y-maze.html.
Z. Wang, K. Murnane, and M. Ghovanloo, “An automated tracking system for Y-maze behavioral test using Kinect depth imaging,” in Proceedings of 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), Turin, Italy, 2017, pp. 1-4. https://doi.org/10.1
F. J. Heredia-Lopez, F. J. Alvarez-Cervera, J. G. Colli-Alfaro, J. L. Bata-Garcia, G. Arankowsky-Sandoval, and J. L. Gongora-Alfaro, “An automated Y-maze based on a reduced instruction set computer (RISC) microcontroller for the assessment of continuous s
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” 2018 [Online]. Available: https://arxiv.org/abs/1804.02767.
Stanford School of Medicine, “Y Maze Spontaneous Alternation Test,” 2023 [Online]. Available: https://med.stanford.edu/sbfnl/services/bm/lm/y-maze.html.
Z. Wang, K. Murnane, and M. Ghovanloo, “An automated tracking system for Y-maze behavioral test using Kinect depth imaging,” in Proceedings of 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), Turin, Italy, 2017, pp. 1-4. https://doi.org/10.1
F. J. Heredia-Lopez, F. J. Alvarez-Cervera, J. G. Colli-Alfaro, J. L. Bata-Garcia, G. Arankowsky-Sandoval, and J. L. Gongora-Alfaro, “An automated Y-maze based on a reduced instruction set computer (RISC) microcontroller for the assessment of continuous s
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” 2018 [Online]. Available: https://arxiv.org/abs/1804.02767.
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