Felix Schürmann

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

The paper creates a detailed computational model of rat somatosensory cortex anatomy, including 4.2 million neurons and 14.2 billion synapses, and identifies limitations in connectivity prediction from morphology.

The paper presents a large-scale biophysically detailed model of somatosensory cortex to simulate and validate cortical dynamics and experimental results.

The paper presents a full-scale in silico model of rat hippocampal CA1 integrating diverse experimental data and validates it through simulations of theta rhythm and other experiments.

The paper enhances brain atlases with laminar coordinate systems and flatmaps to better represent layered brain regions, enabling improved visualization and modeling of structures like the barrel cortex.

STEPS 4.0 improves scalability, performance, and memory efficiency for stochastic reaction-diffusion simulations of neurons.

Ultraliser creates high-fidelity 3D models of neuroscientific structures for in silico simulations, enabling detailed structural analysis and scalable experiments.

The paper identifies hub neurons in a rodent neocortical microcircuit and shows that their removal disrupts network topology and dynamics more than random neuron removal.

The paper digitally reconstructs the neuro-glia-vascular system in rat somatosensory cortex to understand astrocyte-vascular-neuron interactions and their anatomical constraints.

The paper introduces Neuron_Reduce, an analytical method that significantly speeds up nonlinear neuron simulations while preserving key dynamics and dendritic computations.

The paper presents a software workflow to create large-scale, realistic volumetric models of neocortical circuits for in silico optical studies.