Andrew M. Shelton

This paper introduces PhysMAP, a framework that uses multimodal electrophysiological data to identify and study cell types in vivo, showing improved alignment with transcriptomic cell types and circuit dynamics.

This paper introduces NEMO, a multimodal contrastive learning method for classifying cell types and brain regions from electrophysiological data, achieving state-of-the-art results on two opto-tagged datasets and the International Brain Laboratory Brain-wide Map dataset.

This paper delineates four subregions within the mouse claustrum complex using fiber-architecture, genetic markers, and connectivity data, providing a reference framework for future studies.

This paper introduces NP Ultra, a high-density electrode that improves neuronal recording yield, detection, and cell type classification in mice.

This study determines the developmental timeline of the mouse claustrum, including neuronal birthdates, spatial localization, and the maturation of its cortical projections.

This study identifies claustrum neurons and their connectivity patterns, showing they integrate inputs from multiple cortical regions and contribute to multimodal sensory processing.

The paper uses microendoscope imaging in mice to show that anterior claustrum Gnb4+ neurons are active during behavior but not during passive sensory stimulation.