  The brain is likely the most complex organ, given the variety of functions it
controls, the number of cells it comprises, and their corresponding diversity.
Studying and identifying neurons, the brain's primary building blocks, is a
crucial milestone and essential for understanding brain function in health and
disease. Recent developments in machine learning have provided advanced
abilities for classifying neurons. However, these methods remain black boxes
with no explainability and reasoning. This paper aims to provide a robust and
explainable deep-learning framework to classify neurons based on their
electrophysiological activity. Our analysis is performed on data provided by
the Allen Cell Types database containing a survey of biological features
derived from single-cell recordings of mice and humans. First, we classify
neuronal cell types of mice data to identify excitatory and inhibitory neurons.
Then, neurons are categorized to their broad types in humans using domain
adaptation from mice data. Lastly, neurons are classified into sub-types based
on transgenic mouse lines using deep neural networks in an explainable fashion.
We show state-of-the-art results in a dendrite-type classification of
excitatory vs. inhibitory neurons and transgenic mouse lines classification.
The model is also inherently interpretable, revealing the correlations between
neuronal types and their electrophysiological properties.
