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Master theses

Current and past ideas and concepts for Master Theses.

Partial Information Decomposition

Subject

The study of information flow is of critical relevance to understand the functioning of complex systems. Most of the times this information is exchanged at multiple temporal and spatial scales. Furthermore, the relevant variables are recorded with multiple modalities.
More conceptually, the information exchange is most of the time non-binary, that is multiple variables share a similar amount of information on a target one. This information can be synergetic, redundant, or independent. E.g many biological systems involve multiple interacting factors affecting an outcome synergistically and/or redundantly, e.g. genetic contribution to a phenotype or the tight interplay of genes within a gene-regulatory network (GRN).

Kind of work

In this thesis we will use a framework called "Partial Information Decomposition" [1] and we will aim to detect joint information flow at multiple scales in multivariate time series. The main application will be neuroelectrical data (Electroencephalographic recordings in cognitive experiments).

Framework of the Thesis

1) M. Wibra et al., “Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition”, entropy, 2017 http://dx.doi.org/10.3390/e19090494
2) Rogers F.Silva, “A statistically motivated framework for simulation of stochastic data fusion models applied to multimodal neuroimaging”, NeuroImage, pp. 92-117, 2014 https://www.sciencedirect.com/science/article/pii/S1053811914003048
3) Robin A.A. Ince, et al., “A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula”, Wiley online library, http://onlinelibrary.wiley.com/doi/10.1002/hbm.23471/abstract
4) Robyn L.Miller , “Detection of relationships among multi-modal brain imaging meta-features via information flow”, Journal of neuroscience methods, pp. 72-80, 2018 https://www.sciencedirect.com/science/article/pii/S0165027017303862

In collaboration with Prof. Daniele Marinazzo (UGhent-Department of data Analysis) daniele.marinazzo@ugent.be

Expected Student Profile

• Following an MSc in a field related to one or more of the following: electrical engineering, computer science, applied mathematics, Biomedical Engineering
• Experience with signal processing.
• Experience with machine learning and statistics.
• Strong programming skills (matlab, R, python, …).
• Ability to write scientific reports and communicate research results at conferences in English.

Promotor

Prof. Hichem Sahli

+32 (0)2 629 2916

hsahli@etrovub.be

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