Topic: neuronal morphologies

The shapes of neuronal morphologies are essential for the dynamical properties of the brain, as their branching patterns govern different functional properties of cell types. However, disagreements on the definition of neuronal classes due to the subjective views of the experts have spawned several efforts to find an objective way of deriving a morphology classification. We combine machine learning with a variety of mathematical tools to group neuronal morphologies into stable classes. We show that different methods perform optimally on different use-cases. Thus, we propose a combination of tr

Networks are widely used as mathematical models of complex systems across many scientific disciplines, and in particular within neuroscience. In this talk, we introduce two aspects of our collaborative research: (1) machine learning and networks, and (2) graph dimensionality. Machine learning and networks. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. We have d