4 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…
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…
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…
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…