Priesemann, Viola

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22.05.2026

Learning Through Noise: Why Subliminal Learning Works and When It Fails

Authors Brockers VC, Ventzke RD, Neuhaus V, Hidalgo-Ogalde B, Priesemann V Journal Arxiv Citation arXiv:2605.23645 Abstract In the context of artificial neural networks, subliminal learning refers to the transfer of task-relevant knowledge or unintended biases from teacher to student models through distillation on task-unrelated input output pairs. Prior explanations tie
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15.05.2026

TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger

Authors Golovin A, Mohr SB, Gottwald AI, Hvid U, Trivedi S, Neto JP, Schneider AC, Priesemann V Journal Arxiv Citation arXiv:2605.15956 Abstract Here we present a massive longitudinal dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups,
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12.12.2025

Optimizing infectious disease mitigation under dynamic conditions

Authors Müller L, Sartori F, Dehning J, Eggl MF, Priesemann V Journal Arxiv Citation arXiv:2512.11454. Abstract Mitigation measures are essential for controlling the spread of infectious diseases during pandemics and epidemics, but they impose considerable societal, individual, and economic costs. We developed a general optimization framework to balance costs related
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24.11.2025

Continuous dynamics of cooperation and competition in social decision-making

Authors Lewen D, Ivanov V, Dehning J, Ruß J, Fischer A, Penke L, Schacht A, Gail A, Priesemann V, Kagan I Journal Communications Psychology Citation Commun Psychol. 2025 Nov 24;3(1):170. Abstract Real-life social interactions often unfold continuously and involve dynamic cooperation and competition, yet most studies rely on discrete games
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04.11.2025

Redundancy Maximization as a Principle of Associative Memory Learning

Authors Blümel M, Schneider AC, Neuhaus V, Ehrlich DA, Graetz M, Wibral M, Makkeh A, Priesemann V Journal Arxiv Citation arXiv:2511.02584 Abstract Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the local computational principles which are required to
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29.10.2025

Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study

Authors Müller L, Mallick P, Marín-Carballo AB, Dönges P, Kettlitz RJN, Klett-Tammen CJ, Kretzschmar M, Priesemann V, Contreras S Journal Proceedings of the National Academy of Sciences of the United States of America Citation Proc Natl Acad Sci U S A. 2025 Nov 4;122(44):e2524944122. Abstract HIV pre-exposure prophylaxis (PrEP) is
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02.04.2025

Power-law adaptation in the presynaptic vesicle cycle

Authors Mikulasch FA, Georgiev SV, Rudelt L, Rizzoli SO, Priesemann V Journal Communications Biology Citation Commun Biol. 2025 Apr 2;8(1):542. Abstract After synaptic transmission, fused synaptic vesicles are recycled, enabling the synapse to recover its capacity for renewed release. The recovery steps, which range from endocytosis to vesicle docking and
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24.03.2025

Societal self-regulation induces complex infection dynamics and chaos

Authors Wagner J, Bauer S, Contreras S, Fleddermann L, Parlitz U, Priesemann V Journal Physical Review Research Citation Phys. Rev. Research 7, 013308. Abstract Classically, endemic infectious diseases are expected to display relatively stable, predictable infection dynamics. Accordingly, basic disease models such as the susceptible-infected-recovered-susceptible model display stable endemic states
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05.03.2025

A general framework for interpretable neural learning based on local information-theoretic goal functions

Authors Makkeh A, Graetz M, Schneider AC, Ehrlich DA, Priesemann V, Wibral M Journal Proceedings of the National Academy of Sciences of the United States of America Citation Proc Natl Acad Sci U S A. 2025 Mar 11;122(10):e2408125122. Abstract Despite the impressive performance of biological and artificial networks, an intuitive
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16.12.2024

Coupled infectious disease and behavior dynamics. A review of model assumptions

Authors Reitenbach A, Sartori F, Banisch S, Golovin A, Calero Valdez A, Kretzschmar M, Priesemann V, Mäs M Journal Reports on Progress in Physics Citation Rep. Prog. Phys. 88 016601. Abstract To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of
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