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Cited by 1,134Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchVisual perception and processing mechanisms

The cost of cortical computation.

Open access
Lennie, Peter·2003·Current Biology
DOI 10.1016/s0960-9822(03)00135-0
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Abstract

Electrophysiological recordings show that individual neurons in cortex are strongly activated when engaged in appropriate tasks, but they tell us little about how many neurons might be engaged by a task, which is important to know if we are to understand how cortex encodes information. For human cortex, I estimate the cost of individual spikes, then, from the known energy consumption of cortex, I establish how many neurons can be active concurrently. The cost of a single spike is high, and this severely limits, possibly to fewer than 1%, the number of neurons that can be substantially active concurrently. The high cost of spikes requires the brain not only to use representational codes that rely on very few active neurons, but also to allocate its energy resources flexibly among cortical regions according to task demand. The latter constraint explains the investment in local control of hemodynamics, exploited by functional magnetic resonance imaging, and the need for mechanisms of selective attention.

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38 of 38 references verified0 citation errors · 0 not checkable

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  • Mechanisms of synaptic homeostasis and plasticity to maintain the number of synapses in check, as well as sparse coding, may be direct consequences of a limiting and size-invariant, fixed energy budget per neuron.

    “Thus, mechanisms of synaptic homeostasis [62] and plasticity to maintain the number of synapses in check (including the decrease of synaptic markers during sleep [63]), as well as sparse coding, with only a small proportion of neurons firing at high frequencies at any moment [49], [64], [65], may be direct consequences to brain function of a limiting and size-invariant, fixed energy budget per”

    From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [49]

  • Increases in neuronal activity are expected to cost more energy.

    “Increases in neuronal activity, with the associated depolarization and repolarization of cell membranes and cycling of transmitters, are expected to cost more energy [23], [49].”

    From Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution (2011) · cites this paper as [49]

  • The two highest costs associated with neuronal activity come from excitatory postsynaptic potentials in the dendritic tree and propagation of action potentials along the axon.

    “The two highest costs associated with neuronal activity come from excitatory postsynaptic potentials in the dendritic tree and propagation of action potentials along the axon (8).”

    From Evolution of increased glia–neuron ratios in the human frontal cortex (2006) · cites this paper as [8]

  • The increased ratio of glia to neurons in the human frontal cortex might be explained by energy expenditures that are correlated with increasing neocortex size, which, in humans, accounts for 44% of the brain's total energy consumption.

    “The increased ratio of glia to neurons in the human frontal cortex might be explained by energy expenditures that are correlated with increasing neocortex size, which, in humans, accounts for 44% of the brain’s total energy consumption (8).”

    From Evolution of increased glia–neuron ratios in the human frontal cortex (2006) · cites this paper as [8]

  • The pattern of enhanced neuronal energy expenditure despite lower mass-specific brain metabolism is consistent with a model predicting that, with increases in brain size, a progressively smaller fraction of the total neuron population may be concurrently active, leading to more modularized information coding.

    “This pattern is consistent with a model that predicts that, with increases in brain size, a progressively smaller fraction of the total neuron population may be concurrently active, leading to more modularized information coding (7, 8).”

    From Evolution of increased glia–neuron ratios in the human frontal cortex (2006) · cites this paper as [8]

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Authors
Lennie, P.
Affiliations
New York University (US)
Corresponding author
Lennie, Peter
ORCID
Lennie, Peter
Venue
Current Biology
Publisher
Elsevier BV
Published
2003-03
Volume
13
Issue
6
Pages
493-497
ISSN
0960-9822 (ISSN-L)
DOI
10.1016/s0960-9822(03)00135-0
OpenAlex
W2075187489
Cited by
1,134
References
38
Citations by year
2026: 60 · 2025: 84 · 2024: 48 · 2023: 81 · 2022: 56 · 2021: 66 · 2020: 51 · 2019: 64 · 2018: 47 · 2017: 50 · 2016: 56 · 2015: 47 · 2014: 52 · 2013: 44 · 2012: 59
FWCI
12.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Current Biology (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institutes of Health (EY04440, EY13079)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
cortical computation, sparse coding, neural representations, energy allocation, hemodynamic management, functional MRI, selective attention
MeSH terms
Action Potentials, Attention, Brain Mapping, Cell Count, Energy Metabolism, Hemodynamics, Humans, Models, Neurological, Synaptic Transmission, Neurons, Time Factors, Linear Models, Neurotransmitter Agents, Neocortex, Excitatory Postsynaptic Potentials

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