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Brain power
Open accessAbstract
The human brain is just 2% of the body’s weight, but 20% of its metabolic load (1⇓–3), and 10 times more expensive per gram than muscle. On the other hand, the brain manages to produce poetry, design spacecraft, and create art on an energy budget of ∼20 W, a paltry sum given that the computer on which this article is being typed requires 80 W. So where in the brain is power consumed, what is it used for, why is it so expensive relative to other costs of living, and how does it achieve its power efficiency relative to engineered silicon? Many classic papers have studied these questions. Attwell and Laughlin (4) developed detailed biophysical estimates suggesting that neural signaling and the postsynaptic effects of neurotransmitter release combined to account for 80% of the brain’s adenosine triphosphate (ATP) consumption, conclusions that are also supported by the overall physiology and anatomy of neural circuits (5, 6). Numerous studies explored the structural and functional consequences of this expenditure for limiting brain size (7) and scaling (8), efficient wiring patterns (9), analog (graded potential) vs. digital (spiking) signaling (10), distributed neural codes (11⇓–13), the distribution of information traffic along nerve tracts and their size distribution (14⇓–16), and computational heterogeneity and efficiency (17). Many of these ideas have been synthesized by Sterling and Laughlin (18) into a set of principles governing the design of brains. Now, in PNAS, Levy and Calvert (19) propose a functional accounting of the power budget of the mammalian brain, suggesting that communication is vastly more expensive than computation, and exploring the functional consequences for neural circuit organization. Levy and Calvert (19) build on the earlier literature by focusing primarily on the relative power committed to different modes of information … [↵][1]1Email: vijay{at}physics.upenn.edu. [1]: #xref-corresp-1-1
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“The mechanism of its emergence is very energy efficient.”
From There is no such thing as conscious artificial intelligence (2025) · cites this paper as Balasubramanian, 2021
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“The brain itself uses approximately 400 kCal per day, which is less than 0.5 kWh”
From There is no such thing as conscious artificial intelligence (2025) · cites this paper as Balasubramanian, 2021
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Irreversibility and heat generation in the computing process
Landauer, R. (1961) · IBM Journal of Research and Development · doi:10.1147/rd.53.0183 · Cited by 4,980
Interplay between Cortical Spreading Depolarization and Seizures
Kramer, D. R., Fujii, T., Ohiorhenuan, I. E., & Liu, C. Y. (2017) · Stereotactic and Functional Neurosurgery · doi:10.1159/000452841 · Cited by 3,721
An Energy Budget for Signaling in the Grey Matter of the Brain
Attwell, D., & Laughlin, S. B. (2001) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-200110000-00001 · Cited by 3,543
Irreversibility and heat generation in the computing process
Landauer, R. W. (2000) · IBM Journal of Research and Development · doi:10.1147/rd.441.0261 · Cited by 2,665
The expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution
Aiello, L. C., & Wheeler, P. (1995) · Current Anthropology · doi:10.1086/204350 · Cited by 2,291
Cellular energy utilization and molecular origin of standard metabolic rate in mammals
Rolfe, D. F., & Brown, G. C. (1997) · Physiological Reviews · doi:10.1152/physrev.1997.77.3.731 · Cited by 2,021
Siesjö, B. K. (1978) · Medical Entomology and Zoology · Cited by 1,426
The cost of cortical computation.
Lennie, P. (2003) · Current Biology · doi:10.1016/s0960-9822(03)00135-0 · Cited by 1,134
CNS energy metabolism as related to function
Ames, A. (2000) · Brain Research Reviews · doi:10.1016/s0165-0173(00)00038-2 · Cited by 663
Analog Versus Digital: Extrapolating from Electronics to Neurobiology
Sarpeshkar, R. (1998) · Neural Computation · doi:10.1162/089976698300017052 · Cited by 496
Research works printed but not linked: 1
- L. Sokoloff, “The metabolism of the central nervous system in vivo” in Neurophysiology, J. Field, H. W. Magoun, V. E. Hall, Eds. (Handbook of Physiology, American Physiological Society, 1960), sec. 1, vol. 3, pp. 1843–1864.
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Ahirwal, H., Mandal, G., Ghosh, M., & Biswas, S. (2025) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon67893.2025.11327321
Quantum biology: The secret behind the extraordinary efficiency of organisms
Madl, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33018-6.00001-4
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Olin-Ammentorp, W. (2026) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ae5088
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Fournier, L. (2024) · theses.fr (ABES) · doi:10.70675/c4950badzdfcez45b6zb5f6zc76fb88880cb
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Jebali, F. (2023) · theses.fr (ABES) · doi:10.70675/b7632edfz3807z4b8fzb462z31a6dcf54161
Jiang, L., Wang, Y., Luo, J., Liu, Y., Zhang, K., Wang, X., Wang, Y., Liu, Y., Sun, S., Shang, L., et al. (2026) · ACS Sensors · doi:10.1021/acssensors.5c03461
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Hamrell, H., Sjögren, T. K., & Ovrén, H. (2026) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ae6369
Metadata
- Authors
- Balasubramanian, V.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Balasubramanian, Vijay
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-08-02
- Volume
- 118
- Issue
- 32
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8364152
- OpenAlex
- W3196841063
- Cited by
- 113
- References
- 28
- Citations by year
- 2026: 28 · 2025: 38 · 2024: 25 · 2023: 19 · 2022: 3
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Simons Foundation (400425)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- brain energy consumption, neural communication, neural computation, energy efficiency, ATP consumption, neural circuit organization, mammalian brain
- MeSH terms
- Brain, Head
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