Pre-prints

2026

• Lober M., Diesmann M., Kunkel S. (2026) Exploiting network topology in brain-scale simulations of spiking neural networks.
  arXiv: 10.48550/arXiv.2602.23274

2025

• Bouss P., Nestler S., Fischer K., Merger C., René A., Helias M. (2025) Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows.
  arXiv: 10.48550/arXiv.2506.12187
  
  
• Ciobanu A., Dahmen D., Strachan J.P., Helias M. (2025) Reduction of interaction order in hard combinatorial optimization via conditionally independent degrees of freedom.
  arXiv: 2512.16726
  
  
• Foos N., Epping B., Grundler J., Ciobanu A., Singh A., Bode T., Helias M., Dahmen D. (2025) Beyond-mean-field fluctuations for the solution of constraint satisfaction problems.
  arXiv: 10.48550/arXiv.2507.10360
  
  
• Gaglioti G., Cardinale A., Lupo C., Nieus T., Marmoreo F., Gutzen R., Denker M., Pigorini A., Masimini M. Sarasso S., Paolucci P.S., de Bonis G. (2025) Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools.
  arXiv: 10.48550/arXiv.2509.12873
  
  
• Golosio B., Tiddia G., Villamar J., Pontisso L., Sergi L., Simula F., Babu P., Pastorelli E., Morrison A., Diesmann M., Lonardo A., Paolucci P. S., Senk J. (2025) Scalable Construction of Spiking Neural Networks using up to thousands of GPUs.
  arXiv: 10.48550/ARXIV.2512.09502
  
  
• Knight J.C., Senk J., Nowotny T. (2025) A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks.
  arXiv: 10.48550/arXiv.2510.19764
  
  
• Korcsak-Gorzo A., Espinoza Valverde J.A., Stapmanns J., Plesser H.E., Dahmen D., Bolten M., van Albada S.J., Diesmann M. (2025) Event-driven eligibility propagation in large sparse networks: efficiency shaped by biological realism.
  arXiv: 10.48550/arXiv.2511.21674
  
  
• Morales-Gregorio A., Gutzen R., Paneri S., Sapountzis P., Kleinjohann A., Grün S., Riehle A., Chen X., Brochier T., Gregoriou G.G., Kilavik B.E., van Albada S.J. (2025) Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex.
  bioRxiv: 2025.05.13.653871
  
  
• Oberste-Frielinghaus J., Ito J., Grün S. (2025) The effect of data preprocessing on spike correlation analysis results.
  bioRxiv: 10.1101/2025.11.28.691090
  
  
• Oberste-Frielinghaus J., Kurth A., Göltz J., Kriener L, Ito J., Petrovici M.A., Grün S. (2025) Synchronization and semantization in deep spiking networks.
  arXiv: 10.48550/arXiv.2508.12975
  
  
• Peraza Coppola G., Helias M., Ringel Z. (2025) Renormalization group for deep neural networks: Universality of learning and scaling laws.
  arXiv: 10.48550/arXiv.2510.25553
  
  
• 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
  
  
• Schutzeichel L., Bauer J., Bouss P., Musall S., Dahmen D., Helias M. (2025) Transient recurrent dynamics shape representations in mice.
  bioRxiv: 10.1101/2025.06.17.659440
  
  
• Zajzon B., Bouhadjar Y., Fabre M., Schmidt F., Ostendorf N., Neftci E., Morrison A., Duarte R. (2025) SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets.
  arXiv: 2512.24977
  
  

2024

• Epping B., René A., Helias M., Schaub MT. (2024) Graph Neural Networks Do Not Always Oversmooth.
  arXiv: 10.48550/arXiv.2406.02269
  
  
• Kurth AC., Albers J., Diesmann M. , van Albada SJ. (2024) Cell-type specific projection patterns promote balanced activity in cortical microcircuits.
  bioRxiv: 10.1101/2024.10.03.616539
  
  
• 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
  
  

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