Validation and analysis of computationally generated axonal shapes


The shapes of biological objects, such as trees, proteins and brain networks, can be described with a variety of mathematical tools. Numerous different cell types with complex branching structures are present in the brain (neurons, microglia, astrocytes, etc.)

and together form complex networks. The relation between the shapes of single cells and the structure of the networks they form is not yet well understood, and the difficulty to study this link in vivo further hampers our ability to investigate it.

The branch of mathematics known as algebraic topology has proved particularly useful for the description of neuronal systems, both in the level of single trees as well as at the level of networks.

We have developed techniques to study the topology of individual neurons based on algebraic topology (Kanari et al. 2018) and to computationally synthesize dendrites (Kanari et al. 2022). We are currently working on a synthesis algorithm for axons that

will allow us to close the loop and form artificial networks from individual cells. In this project we will develop a validation framework to ensure the high quality of computationally generated axons. We are looking for a highly motivated student with experience

in Python programming and mathematics to implement the validation of axonal shapes.



Category Semester or Master’s Project
Type 50% theory – 50% software
Supervisor Lida Kanari