The student will investigate the relationship between network topology and function of biological networks and how variations of these properties lead to variations in behaviour.

Impact of local topology and local adaptation on network behaviour

Computational Neuroscience
Dambre
Ghent University
Ghent
Belgium

The student will start from existing biological network data and network models provided by other partners (F. Zeldenrust, P. Poirazi, G. Debregeas) and investigate the specificity of the relationship between local/global topology and network activity/computation. For this, we will thoroughly evaluate and combine existing quantitative measures of graph topology (e.g., node degrees, distance-based connectivity, motifs, hierarchy-based measures like Rent’s exponent) and graph similarity (e.g., graph kernels used in machine learning) and devise new ones that are more appropriate to biological networks. He/she will use these measures in the provided network simulation models to quantify the sensitivity of network activity and information processing to variations in these measures, or in other words, how well ‘abnormal’ behaviour can be explained based on variations in these structural network measures. From these experiments, we will derive probabilistic models that express the relationship between local/global parameter variability and variability in the network activity patterns and information processing.

More information: https://www.ugent.be/en/work/vacancies/scientific/phd-student-lo8js

Expected Results:

1. Determine the  relationship between topology and network activity patterns.

2. Determine the relationship between topology and network information processing.

3. Identify useful quantitative measures and probabilistic models that express this relationship and use them  to identify structures that will display ‘abnormal’ behaviour.

Planned secondment(s):

1. Radboud University, Fleur Zeldenrust: M11-13: get acquainted with network data and network models.

2. IMBB, FORTH, Panayiota Poirazi, M27-30: apply developed models on pre- and post-learning networks.

Enrolment in Doctoral degree(s): You will be enrolled at Ghent University PhD programme