Deep Embedding Clustering for the visual cortex cells embeddings

Motivation

We train models to predict the visual cortex responses for mice (a model takes an image or a video as input and predicts the neuronal response). We treat part of the model weights as neuronal embeddings (each array has correspondences for the actual recorded cells). The idea is that using these cell embeddings we can come up with the desired cell type classification using unsupervised clustering on top of the embeddings. Currently, a big issue is that classic clustering algorithms are vulnerable to the curse of dimensionality, so we have to come up with an alternative clustering, maybe implying a model finetuning to make the clusters more separable.

Project

The idea is to check if neuron performance is related to the resulting clusters using Deep Embedding Clustering, where the idea is to adjust the weights to make the embeddings more separatable. The original paper is done based on autoencoders, while in our case the embeddings are the network’s weights. To estimate the quality of results we would like to compare with classic k-means, GMM, as well as spectral clustering methods (like Spectral Embedded Clustering, Nie et al. 2011, and local discriminant models and global integration (LDMGI), Yang et al. 2010).

Thesis

Within the context of this project, several research questions could be explored: - Is neuron performance is connected to the resulting clustering? - How to adjust DEP for the rotation-equivariant models for DEP? - Does DEP finetuning ruin the original model’s performance? If yes, how to come up with a joint loss to keep performance? - Does DEP improve clustering? - What is the optimal amount of clusters?

Contact

To apply please email Polina Turishcheva stating your interest in this project and detailing your relevant skills. A part of this project could be also a lab rotation.