Sophie Laturnus

The paper introduces MorphVAE, a variational autoencoder for generating neural morphologies from 3D-walks, showing improved cell type discrimination and efficient morphology generation.

The paper combines morpho-electric and transcriptomic data from mouse motor cortex neurons to show that transcriptomic types within families form continuous morpho-electric landscapes rather than discrete entities.

The paper evaluates different neuronal morphology representations for cell type discrimination using curated datasets of mouse neurons, finding 2D density maps and morphometric statistics to be the most effective.

The paper identifies distinct cell types and circuit organization in layer 4 of primary sensory areas in adult mouse neocortex, challenging the canonical microcircuit model.