Philipp Hornauer

The paper develops a classifier to identify excitatory and inhibitory neurons using HD-MEA recordings without post-hoc labeling, revealing cell-type-specific network dynamics.

The paper programs human neuron subtypes using combinatorial patterning and scRNA-seq to identify diverse neuron types and their developmental signaling networks.

This study identifies homophilic wiring as a principle governing neuronal network topology in vitro, outperforming traditional models in explaining connectivity patterns.

This paper introduces an ensemble artificial neural network (eANN) to improve neuronal connectivity inference from spike trains, showing it outperforms existing methods across different dynamical regimes.

This paper introduces FDDC, a method for inferring connectivity in neuronal networks, showing it outperforms existing methods and reveals a negative correlation between small-worldness and network synchrony.

The paper introduces DeePhys, a tool for functional phenotyping of neurons using electrophysiological data, and shows it can predict genotype and age in iPSC-derived DA neurons with the A53T SNCA mutation.