Deep-learning architectures for analysing population neural data
| Thesis title in Czech: | Architektury hlubokého učení pro analýzu populačních neaurálních dat |
|---|---|
| Thesis title in English: | Deep-learning architectures for analysing population neural data |
| Key words: | hluboké učení|výpočetní neurověda|modelování V1|biologicky inspirované architektury |
| English key words: | deep-learning|computational neuroscience|v1 modeling|bio-inspired architectures|visual computation |
| Academic year of topic announcement: | 2019/2020 |
| Thesis type: | diploma thesis |
| Thesis language: | angličtina |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | Mgr. Ján Antolík, Ph.D. |
| Author: | Mgr. Petr Houška - assigned and confirmed by the Study Dept. |
| Date of registration: | 05.01.2020 |
| Date of assignment: | 06.02.2020 |
| Confirmed by Study dept. on: | 09.11.2020 |
| Date and time of defence: | 04.02.2021 08:30 |
| Date of electronic submission: | 04.01.2021 |
| Date of submission of printed version: | 04.01.2021 |
| Date of proceeded defence: | 04.02.2021 |
| Opponents: | doc. Mgr. Martin Pilát, Ph.D. |
| Guidelines |
| In this project student will develop novel deep-learning architectures based around the NDN (htps://github.com/NeuroTheoryUMD/NDN) suite of deep models, targeting population data recorded from mammalin primary visual cortex. The student will expand the library with parametrized kernel features, and subsequently testing their combination with deep architectures on neural population recordings from mouse and/or cat primary visual cortex. |
| References |
| [1] Willmore, B. D. B., & Smyth, D. (2003). Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes. Network, 14(3), 553–77. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12938771
[2] Vintch, B., Movshon, J. A., & Simoncelli, E. P. (2015). A Convolutional Subunit Model for Neuronal Responses in Macaque V1. Journal of Neuroscience, 35(44), 14829–14841. https://doi.org/10.1523/JNEUROSCI.2815-13.2015 [3] Dan A. Butts (2019). Data-driven approaches to understanding visual neuron activity. Annual Review of Vision Science, 5:451-457, [4] Antolík, J., Hofer, S. B., Bednar, J. A., & Mrsic-Flogel, T. D. (2016). Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes. PLoS Computational Biology, 12(6). https://doi.org/10.1371/journal.pcbi.1004927 |