Redundancy maximization as a principle of associative memory learning in Hopfield networks
- DOI
- 10.1038/s41467-026-78777-2
- Published
- 2026-11-06
- Container
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41467-026-78777-2,
title = {Redundancy maximization as a principle of associative memory learning in Hopfield networks},
author = {Mark Blümel and Andreas C. Schneider and Valentin Neuhaus and David A. Ehrlich and Marcel Graetz and Michael Wibral and Abdullah Makkeh and Viola Priesemann},
year = {2026},
journal = {Nature Communications},
doi = {10.1038/s41467-026-78777-2},
url = {https://doi.org/10.1038/s41467-026-78777-2}
}RIS
TY - JOUR TI - Redundancy maximization as a principle of associative memory learning in Hopfield networks AU - Mark Blümel AU - Andreas C. Schneider AU - Valentin Neuhaus AU - David A. Ehrlich AU - Marcel Graetz AU - Michael Wibral AU - Abdullah Makkeh AU - Viola Priesemann PY - 2026 JO - Nature Communications DO - 10.1038/s41467-026-78777-2 UR - https://doi.org/10.1038/s41467-026-78777-2 ER -
APA
Blümel, M., Schneider, A. C., Neuhaus, V., Ehrlich, D. A., Graetz, M., Wibral, M., Makkeh, A., & Priesemann, V. (2026). Redundancy maximization as a principle of associative memory learning in Hopfield networks. Nature Communications. https://doi.org/10.1038/s41467-026-78777-2
Source records
- crossref · retrieved 2026-11-10T14:18:30.108Z