Rozšíření existujícího modelu primárního vizuálního kortexu o NMDA kanály
| Název práce v češtině: | Rozšíření existujícího modelu primárního vizuálního kortexu o NMDA kanály |
|---|---|
| Název v anglickém jazyce: | Extension of existing model of primary visual cortex with NMDA channels |
| Akademický rok vypsání: | 2024/2025 |
| Typ práce: | diplomová práce |
| Jazyk práce: | |
| Ústav: | Katedra softwaru a výuky informatiky (32-KSVI) |
| Vedoucí / školitel: | Mgr. Ján Antolík, Ph.D. |
| Řešitel: |
| Zásady pro vypracování |
| The goal of this project is to upgrade our model of the primary visual cortex [1] so that NMDA receptors can be implemented in it. Currently, the only type of excitatory synaptic receptors existing in our model are AMPA, which are transient, whereas NMDA receptors have a longer rise time and decay time and would allow us to model some more sustained dynamics. Implementing them in our model would require some modifications in the python library (Mozaik) [2] we have developed in our group, so that it allows the creation of different categories of excitatory synapses with different temporal dynamics. Once the required new model elements are implemented the student will update our previos model to contain the new channels. Next student will perform parameter search on the new model, to qualitatively match dynamics of our previous model. |
| Seznam odborné literatury |
| [1] Antolík, J, Monier, C., Davison, A., Frégnac Y.; A comprehensive data-driven model of cat primary visual cortex. bioRxiv, 416156
[2] Antolík, J., & Davison, A. P. (2013). Integrated workflows for spiking neuronal network simulations. Frontiers in Neuroinformatics, 7(December), 1–15. https://doi.org/10.3389/fninf.2013.00034 [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 [4] Roth, A., & van Rossum, M. C. (2009). Modeling synapses. Computational modeling methods for neuroscientists, 6, 139-160. |