  Recent developments in high throughput profiling of individual neurons have
spurred data driven exploration of the idea that there exist natural groupings
of neurons referred to as cell types. The promise of this idea is that the
immense complexity of brain circuits can be reduced, and effectively studied by
means of interactions between cell types. While clustering of neuron
populations based on a particular data modality can be used to define cell
types, such definitions are often inconsistent across different
characterization modalities. We pose this issue of cross-modal alignment as an
optimization problem and develop an approach based on coupled training of
autoencoders as a framework for such analyses. We apply this framework to a
Patch-seq dataset consisting of transcriptomic and electrophysiological
profiles for the same set of neurons to study consistency of representations
across modalities, and evaluate cross-modal data prediction ability. We explore
the problem where only a subset of neurons is characterized with more than one
modality, and demonstrate that representations learned by coupled autoencoders
can be used to identify types sampled only by a single modality.
