Abstract
Sleep is an essential process for the survival of animals. However, its phenomenon is poorly understood. To understand the phenomenon of sleep, the analysis should be made from the activities of a large number of cortical neurons. Calcium imaging is a recently developed technique that can record a large number of neurons simultaneously, however, it has a disadvantage of low time resolution. In this paper, we aim to discover phenomena which characterize sleep/wake states from calcium imaging data. We made an assumption that groups of neurons become active simultaneously and the neuronal activities of groups differ between sleep and wake states. We used non-negative matrix factorization (NMF) to identify those groups and their neuronal activities in time from calcium imaging data. NMF was used because neural activity can be expressed by the sum of individual neuronal activity and fluorescence intensity data are always positive values. We found that there are certain groups of neurons that behave differently between sleep and wake states.
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Acknowledgements
This work was supported by Grants-in-Aid for Scientific Research (KAKENHI), Japan Society for the Promotion of Science (JSPS) (Grant Number 16K18358 to T.K.; 26220207 to T.K. and M.Y.; 19K12111 to H.H; 17H06095 to M.Y.); World Premier International Research Center Initiative (WPI), the Ministry of Education, Culture, Sports, Science and Technology (MEXT) (to M.Y.); Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST) (Grant Number JPMJCR1761 to H.H.; JPMJCR1655 to M.Y.); Yamada Research Grant (to T.K.), Takeda Science Foundation (to M.Y.), and Uehara Memorial Foundation (to M.Y.).
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Nagayama, M. et al. (2019). Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_8
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