651 citations · 2.5k across the 55 of their papers we have counts for
55 papers
Network Information Enhances Unreliable News Domain Detection
Raphaela Keßler, Roman David Ventzke, Viola Priesemann +1
Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag…
Learning Through Noise: Why Subliminal Learning Works and When It Fails
Vincent C. Brockers, Roman D. Ventzke, Valentin Neuhaus +2
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 dist…
Conformity Generates Collective Misalignment in AI Agents Societies
Giordano De Marzo, Alessandro Bellina, Claudio Castellano +2
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations wh…
Vocabulary shapes cross-lingual variation of word-order learnability in language models
Jonas Mayer Martins, Jaap Jumelet, Viola Priesemann +1
Why do some languages like Czech permit free word order, while others like English do not? We address this question by pretraining transformer language models on a spectrum of synt…
Finite integration time can shift optimal sensitivity away from criticality
Sahel Azizpour, Viola Priesemann, Johannes Zierenberg +1
Sensitivity to small changes in the environment is crucial for many real-world tasks, enabling living and artificial systems to make correct behavioral decisions. It has been shown…
Representation learning in cerebellum-like structures
Lucas Rudelt, Fabian Mikulasch, Viola Priesemann +1
Animals use past experiences to adapt future behavior. To enable this rapid learning, vertebrates and invertebrates have evolved analogous neural structures like the vertebrate cer…
Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks
Mark Blümel, Andreas C. Schneider, Valentin Neuhaus +5
Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the local computational princip…
On the Statistical Physics of Wealth Distribution
Joel Wagner, Siew Ann Cheong, Viola Priesemann
Asset exchange models (AEMs) provide a physics-inspired framework for studying wealth formation. These models capture wealth distribution dynamics via pairwise money exchanges, yie…
Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models
Vincent C. Brockers, David A. Ehrlich, Viola Priesemann
Large Language Models are increasingly used to simulate human opinion dynamics, yet the effect of genuine interaction is often obscured by systematic biases. We develop a Bayesian…
Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study
Laura Müller, Piklu Mallick, Antonio B. Marín-Carballo +6
HIV pre-exposure Prophylaxis (PrEP) has become essential for global HIV control, but its implementation coincides with rising bacterial STI rates among men who have sex with men (M…
Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program
Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz +50
This review synthesizes advances in predictive processing within the sensory cortex. Predictive processing theorizes that the brain continuously predicts sensory inputs, refining n…
What should a neuron aim for? Designing local objective functions based on information theory
Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich +4
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological system…
Available observation time regulates optimal balance between sensitivity and confidence
Sahel Azizpour, Viola Priesemann, Johannes Zierenberg +1
Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response. In particular, fast decisions need to rely…
A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions
Abdullah Makkeh, Marcel Graetz, Andreas C. Schneider +3
Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions…
Societal self-regulation induces complex infection dynamics and chaos
Joel Wagner, Simon Bauer, Sebastian Contreras +3
Classically, endemic infectious diseases are expected to display relatively stable, predictable infection dynamics. Accordingly, basic disease models such as the susceptible-infect…
Signatures of hierarchical temporal processing in the mouse visual system
Lucas Rudelt, Daniel González Marx, F. Paul Spitzner +3
A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic m…
Model-based assessment of sampling protocols for infectious disease genomic surveillance
Sebastian Contreras, Karen Y. Oróstica, Anamaria Daza-Sanchez +8
Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated…
Impact of the Euro 2020 championship on the spread of COVID-19
Jonas Dehning, Sebastian B. Mohr, Sebastian Contreras +5
Large-scale events like the UEFA Euro~2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number a…
SIR-Model for Households
Philipp Doenges, Thomas Götz, Tyll Krueger +3
Households play an important role in disease dynamics. Many infections happening there due to the close contact, while mitigation measures mainly target the transmission between ho…
Tackling the subsampling problem to infer collective properties from limited data
Anna Levina, Viola Priesemann, Johannes Zierenberg
Complex systems are fascinating because their rich macroscopic properties emerge from the interaction of many simple parts. Understanding the building principles of these emergent…
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition
David A. Ehrlich, Andreas C. Schneider, Viola Priesemann +2
In neural networks, task-relevant information is represented jointly by groups of neurons. However, the specific way in which this mutual information about the classification label…
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware
Benjamin Cramer, Markus Kreft, Sebastian Billaudelle +9
A unique feature of neuromorphic computing is that memory is an implicit part of processing through traces of past information in the system's collective dynamics. The extent of me…
Modular architecture facilitates noise-driven control of synchrony in neuronal networks
Hideaki Yamamoto, F. Paul Spitzner, Taiki Takemuro +9
Brain functions require both segregated processing of information in specialized circuits, as well as integration across circuits to perform high-level information processing. One…
Intrinsic timescales of spiking activity in humans during wakefulness and sleep
Annika Hagemann, Marcel Stephan Kehl, Jonas Dehning +5
Information processing in the brain requires integration of information over time. Such an integration can be achieved if signals are maintained in the network activity for the req…