Summary
Many biological systems can be seen as networks of interconnected oscillating units. Unraveling how they interact, coordinate and influence each other in a random environment is essential for understanding systems such as the dynamic brain. The problem is that the current statistical methods are insufficient for solid data analysis. To solve this problem, I will elaborate the methodology of cointegration analysis, which has been applied mainly in econometrics, to provide a principled statistical framework for analyzing experimental data from neuronal systems. I will work on this project under the supervision of Professor Susanne Ditlevsen at the University of Copenhagen (UCPH).
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/887784 |
| Start date: | 01-09-2021 |
| End date: | 31-08-2023 |
| Total budget - Public funding: | 207 312,00 Euro - 207 312,00 Euro |
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Original description
Many biological systems can be seen as networks of interconnected oscillating units. Unraveling how they interact, coordinate and influence each other in a random environment is essential for understanding systems such as the dynamic brain. The problem is that the current statistical methods are insufficient for solid data analysis. To solve this problem, I will elaborate the methodology of cointegration analysis, which has been applied mainly in econometrics, to provide a principled statistical framework for analyzing experimental data from neuronal systems. I will work on this project under the supervision of Professor Susanne Ditlevsen at the University of Copenhagen (UCPH).Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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