Abstract
The study of the relationship between brain neuron activity and behavioral responses of humans and other animals is an area of interest, although it has received relatively little attention from scientific biology and medical research centers. In this paper, we consider the problem of determining a mouse position in a circular track based on its neural activity data, and investigate the use of machine learning for solving this problem. The study is conducted in two parts: a classification task, where the model predicts which sector of the track the mouse is in at a particular time, and a regression task, where it predicts exact coordinates for each time step. We propose a neural network-based solution for both tasks, based on a graph of brain neuron activity. Accuracy results were obtained: 89% for classification and 93% for regression.
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Data and Code Availability
All data, code and launch scripts used for the article is provided as part of the replication package. It is available at https://github.com/nastyalabs/mouseBrain.
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Acknowledgments
This research was funded by the “Center of Photonics” funded by the Ministry of Science and Higher Education of the Russian Federation (contract no. 075-15-2022-293).
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Vodeneeva, A. et al. (2024). Determining Mouse Behavior Based on Brain Neuron Activity Data. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14835. Springer, Cham. https://doi.org/10.1007/978-3-031-63772-8_11
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DOI: https://doi.org/10.1007/978-3-031-63772-8_11
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