Metody strojového učení v počítačové neurovědě
| Thesis title in Czech: | Metody strojového učení v počítačové neurovědě |
|---|---|
| Thesis title in English: | Machine learning methods in computational neuroscience |
| Key words: | strojove uceni|neuroveda |
| English key words: | machine learning|neuroscience |
| Academic year of topic announcement: | 2023/2024 |
| Thesis type: | dissertation |
| Thesis language: | |
| Department: | Department of Applied Mathematics (32-KAM) |
| Supervisor: | Mgr. Martin Schmid, Ph.D. |
| Author: | |
| Advisors: | Mgr. Ján Antolík, Ph.D. |
| doc. RNDr. Martin Balko, Ph.D. | |
| Guidelines |
| The goal is to investigate the application of machine learning methods for imperfect information environments in various domains such as computational neuroscience. In particular, to explore and develop methods that could be used for development of future visual prosthetic systems.
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| References |
| Antolík, J., Monier, C., Frégnac, Y. & Davison, A. P. A comprehensive data-driven model of cat primary visual cortex. bioRxiv 416156–416156 (2018) doi:10.1101/416156.
Borda, E. & Ghezzi, D. Advances in visual prostheses: engineering and biological challenges. Prog. Biomed. Eng. 4, 032003 (2022). Brown, N., Bakhtin, A., Lerer, A., and Gong, Q. Combining deep reinforcement learning and search for imperfect-information games. Advances in Neural Information Processing Systems 33 (2020), 17057–17069. |