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Cited by 113Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchNeural Networks and Applications

The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.

Brain power

Open access
Balasubramanian, Vijay·2021·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.2107022118PMC PMC8364152
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Abstract

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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  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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Metadata

Authors
Balasubramanian, V.
Affiliations
University of Pennsylvania (US)
Corresponding author
Balasubramanian, Vijay
ORCID
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
DOI
10.1073/pnas.2107022118
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
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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