Learning with artificial and natural neural networks: trade-offs in energy consumption and representations
Decades after Hopfield and Hinton’s seminal works on neural computation and Boltzmann machines, the use of neural networks in machine learning has revolutionized artificial intelligence. Physics, with the help of neuroscience, still has a lot to say on many issues. We here discuss two of them, energy consumption and representations.
Authors
- Simona Cocco (ORCID: https://orcid.org/0000-0002-1852-7789)
- Rémi Monasson (ORCID: https://orcid.org/0000-0002-4459-0204)
Institutions
- Centre National de la Recherche Scientifique (FR)
- École Normale Supérieure - PSL (FR)
- École Normale Supérieure (BI)
- Laboratoire de Physique de l'Ecole Normale Supérieure (FR)
Publication Details
- Journal
- Europhysics news
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1051/epn/2025109
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00