  Given the complex geometry of white matter streamlines, Autoencoders have
been proposed as a dimension-reduction tool to simplify the analysis
streamlines in a low-dimensional latent spaces. However, despite these recent
successes, the majority of encoder architectures only perform dimension
reduction on single streamlines as opposed to a full bundle of streamlines.
This is a severe limitation of the encoder architecture that completely
disregards the global geometric structure of streamlines at the expense of
individual fibers. Moreover, the latent space may not be well structured which
leads to doubt into their interpretability. In this paper we propose a novel
Differentiable Vector Quantized Variational Autoencoder, which are engineered
to ingest entire bundles of streamlines as single data-point and provides
reliable trustworthy encodings that can then be later used to analyze
streamlines in the latent space. Comparisons with several state of the art
Autoencoders demonstrate superior performance in both encoding and synthesis.
