Deep Learning Tools for Next-Generation Connectomics

Abstract

In recent years, the field of connectomics has witnessed exciting developments. Efficient algorithms are being developed to reconstruct nanoscale maps of large-scale images, allowing us a better understanding of how neural tissue computes. However, our ability to build powerful tools for the next generation of connectomics is dependent on navigating an inherent accuracy v.s. speed v.s. scalability trade-off.

This thesis addresses this tradeoff by introducing four deep learning tools and techniques applied to the acquisition, reconstruction and modeling stages of connectomics pipelines. First, we propose a way to speed up the acquisition of images using learning-guided electron microscope (EM). Second, we proposed a faster and more scalable 3D reconstruction algorithm -- cross-classification clustering (3C), for large-scale connectomics datasets. Third, we introduce a cross-modality image translation technique mapping fast X-ray images to EM images with enhanced segmentation quality. Finally, we introduced a technique to bridge the gaps between structural and functional data with connectome-constrained latent variable models (CC-LVMs) of the unobserved voltage dynamics for the whole-brain nervous system. We hope these advanced applications of deep learning techniques will help address the performance and accuracy trade-offs of next-generation connectomics studies.