Pre-prints

2025

• Quercia A., Cao Z., Bangun A., Paul RD., Morrison A., Assent I., Scharr H. (2025) 1LoRA: Summation Compression for Very Low-Rank Adaptation.
  arXiv: 10.48550/ARXIV.2503.08333
  
  
• Quercia A., Yildiz E., Cao Z., Krajsek K., Morrison A., Assent I., Scharr H. (2025) Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks.
  arXiv: 10.48550/ARXIV.2501.12824
  
  
• Rathore O., Paul R., Morrison A., Scharr H., Pfaehler E. (2025) Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation.
  arXiv: 10.48550/arxiv.2503.22271
  
  
• Ringel Z., Rubin N., Mor E., Helias M., Seroussi I. (2025 )Applications of Statistical Field Theory in Deep Learning.
  arXiv: 10.48550/arxiv.2502.18553
  
  
• Rubin N., Fischer K., Lindner J., Dahmen D., Seroussi I., Ringel Z., Krämer M., Helias M. (2025) From Kernels to Features: A Multi-scale Adaptive Theory of Feature Learning.
  arXiv: 10.48550/arXiv.2502.03210
  
  
• Senk J., Kurth A., Furber S., Gemmeke T., Golosio B., Heittmann A., Knight JC., Müller E., Noll T., Nowotny T., Coppola GP., Peres L., Rhodes O., Rowley A., Schemmel J., Stadtmann T., Tetzlaff T., Tiddia G., J VAS., Villamar J., Diesmann M. (2025) Constructive community race: full-density spiking neural network model drives neuromorphic computing.
  arXiv: 2505.21185
  
  

2024

• Albers J., Kurth A.C., Robin Gutzen R., Morales-Gregorio A., Denker M., Grün S., van Albada S.J., Diesmann M. (2024) Assessing the similarity of real matrices with arbitrary shape.
  arXiv: 10.48550/arXiv.2403.17687
  
  
• Epping B., René A., Helias M., Schaub MT. (2024) Graph Neural Networks Do Not Always Oversmooth.
  arXiv: 10.48550/arXiv.2406.02269
  
  
• Jiang H.-J., Aćimović J., Manninen T., Ahokainen I., Stapmanns J., Lehtimäki M., Diesmann M., van Albada SJ., Plesser HE., Linne M.-L. (2024) Modeling Neuron-astrocyte Interactions in Neural Networks Using Distributed Simulation.
  bioRxiv: 10.1101/2024.11.11.622953
  
  
• Köhler CA., Grün S., Denker M. (2024) Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO).
  arXiv: 10.48550/ARXIV.2412.05021
  
  
• Kurth AC., Albers J., Diesmann M. , van Albada SJ. (2024) Cell-type specific projection patterns promote balanced activity in cortical microcircuits.
  bioRxiv: 2024.10.03.616539
  
  
• Ness TV., Tetzlaff T., Einevoll GT., Dahmen D. (2024) On the validity of electric brain signal predictions based on population firing rates.
  bioRxiv: 10.1101/2024.07.10.602833
  
  
• Oberste-Frielinghaus J., Morales-Gregorio A., Essink S., Kleinjohann A., Grün S., Ito J. (2024) Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings.
  bioRxiv: 10.1101/2024.01.11.575181
  
  
• Pronold J., Morales-Gregorio A., Rostami V., van Albada SJ. (2024) Cortical multi-area model with joint excitatory-inhibitory clusters accounts for spiking statistics, inter-area propagation, and variability dynamics.
  bioRxiv: 10.1101/2024.01.30.577979
  
  
• Villamar J., Kelbling M., More HL., Denker M., Tetzlaff T., Senk J., Thober S. (2024) Metadata practices for simulation workflows. arXiv: 10.48550/arXiv.2408.17309
  
  

2023

• Fischer K., David D., Helias M. (2023) Optimal signal propagation in ResNets through residual scaling.
  arXiv: 10.48550/arXiv.2305.07715
  
  

2022

• Keup C., Helias M. (2022) Origami in N dimensions: How feed-forward networks manufacture linear separability.
  arXiv: 10.48550/arXiv.2203.11355
  
  
• Kleinjohann A., Berling D., Stella A., Tetzlaff T., Grün S. (2022) Model of multiple synfire chains explains cortical spatio-temporal spike patterns.
  bioRxiv: 10.1101/2022.08.02.502431
  
  
• Korcsak-Gorzo A., Linssen C., Albers J., Dasbach S., Duarte R., Kunkel S., Morrison A., Senk J., Stapmanns J., Tetzlaff T., Diesmann M., van Albada SJ. (2022) Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density. arXiv: 10.48550/arXiv.2212.05354
  
  
• Stubenrauch J., Keup C., Kurth AC., Helias M., van Meegen A. (2022) Phase Space Analysis of Chaotic Neural Networks.
  arXiv: 10.48550/arXiv.2210.07877