Aplikace umělých neuronových sítí pro charakterizaci kódování vizuální informace v kortikálních neuronech a pro vývoj stimulačních protokolů pro protetické vidění.
| Thesis title in Czech: | Aplikace umělých neuronových sítí pro charakterizaci kódování vizuální informace v kortikálních neuronech a pro vývoj stimulačních protokolů pro protetické vidění. |
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| Thesis title in English: | Applications of artificial neural networks to characterize visual information encoding in cortical neurons and to develop stimulation protocols for prosthetic vision. |
| Academic year of topic announcement: | 2023/2024 |
| Thesis type: | dissertation |
| Thesis language: | čeština |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | Mgr. Ján Antolík, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 20.03.2024 |
| Date of assignment: | 20.03.2024 |
| Confirmed by Study dept. on: | 21.03.2024 |
| Guidelines |
| Tens of millions of people around the world are blind. Recent bio-technological advances offer hope for restoration of vision in blind patients via a prosthetic device that taps directly into the cortex, where it feeds information from a head mounted camera. While all the technological components of the visual prosthesis are still under development, an important question remains open: how to stimulate the cortex to elicit precepts that are close to those due to the perception of the given stimulus under normal vision. The goal of this project is to use artificial neural networks (ANNs) techniques to learn the transformation from natural image movies into encoding of the images in neurons in the primary visual cortex. Such learned transformation should then be tested in our large-scale model of primary visual cortex, to determine the ability to recreate the encoding of natural images in cortex. |
| References |
| [1] Antolík, J, Monier, C., Davison, A., Frégnac Y.; A comprehensive data-driven model of cat primary visual cortex. bioRxiv, 416156
[2] 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, 1–22. [3] Antolik, J., Sabatier, Q., Galle, C., & Frégnac Y. (2019). Cortical visual prosthesis : a detailed large-scale simulation study. [4] Dayan & Abbott (2005) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems |