Embeddings of Unbranched Segments of Neuronal Dendrites for Neuron Clustering
The brain is structured into various areas that each contain different types of neurons. Clustering of such cortical neurons by their shape (including its branching structure) has a long tradition in biology research. It is known that the shape of a neuron is tightly connected to its function in the brain. In the last couple of years, the amount of data has risen massively such that manual inspection is no longer feasible. We are thus interested in purely data-driven methods to cluster such neurons. Unsupervised or semi-supervised techniques could allow to make use of all the available data and provide high-accuracy clusterings and classifications without relying (too much) on domain knowledge.
The aim of this project is to focus on embedding segments of dendrites as a future building block for new algorithms that are able to embed cortical neurons. The task is to build a model that can effectively embed 3D point clouds of unbranched dendrite segments in a way that key features are maintained. It is then possible to test those features in “real life” by substituting the hand-crafted edge features in  by the newly developed embeddings.
- Python programming
- Interest in 3D data
- Interest in working with neurons and biological data
- Experience with PyTorch and/or Tensorflow is a plus
 Graph Representation Learning for Large-Scale Neuronal Morphological Analysis