| Grant number: | 23/07481-6 |
| Support Opportunities: | Regular Research Grants |
| Start date: | March 01, 2024 |
| End date: | February 28, 2026 |
| Field of knowledge: | Physical Sciences and Mathematics - Physics |
| Principal Investigator: | Thomas Kaue Dal Maso Peron |
| Grantee: | Thomas Kaue Dal Maso Peron |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Abstract
Synchronization processes are crucial for the proper functioning ofbiological systems. However, coherent behaviors can also create pernicious effects, such as the pathological synchrony in the brain, which is related with neuronal disorders such as Parkinson disease and autism. In the complex systems literature, the collective dynamics of populations of neurons is typically studied by assessing how the formation of synchronous states depends on the network of synapses. In this project, we will tackle the inverse problem that is typically found in experimental scenarios, that is, (i) the reconstruction of dynamical networks. We will develop a method that employs ergodic theory and machine learning to solve the opposite problem: given a set of time series, it will reveal the connections of the network that generated the data. This methodology will allow us to analyze experimental recordings in neuroscience. As an application of our technique, we will (ii) characterize and classify neurological diseases by using clinical data recorded in EEG exams of children with autism. (AU)
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