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.

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-03-24
DOI
https://doi.org/10.1051/epn/2025109/pdf
Primary Topic
Wheat and Barley Genetics and Pathology
Type
article
Field-Weighted Citation Impact
0.00
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article

Learning with artificial and natural neural networks: trade-offs in energy consumption and representations

Springer Link (Chiba Institute of Technology)
Wheat and Barley Genetics and Pathology
article

Learning with artificial and natural neural networks: trade-offs in energy consumption and representations

article en

Abstract

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.

Springer Link (Chiba Institute of Technology)
Affordable and clean energy
Openalex Percentile: Top 41%
Wheat and Barley Genetics and Pathology
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Learning with artificial and natural neural networks: trade-offs in energy consumption and representations · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS