12 citations · 23 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
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…
Shannon invariants: A scalable approach to information decomposition
Aaron J. Gutknecht, Fernando E. Rosas, David A. Ehrlich +3
Distributed systems, such as biological and artificial neural networks, process information via complex interactions engaging multiple subsystems, resulting in high-order patterns…