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

Neuronal Cell Type Classification using Deep Learning