An Energy Budget for Signaling in the Grey Matter of the Brain

By David Attwell, Simon Barry Laughlin. 2001

3,522 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “By contrast, the mushroom body and the dentate gyrus may be able to maintain sparser representations, which are more energetically efficient (Attwell and Laughlin, 2001).”
Recursive ICA (2006, background): “This is important for the survival of the biological system, considering the fact that human brain consists 2% of the body weight but accounts for 20% of its resting metabolism [14].”
Physiology of Astroglia. (2018, neutral): “It has been postulated that neurons utilize ~90–95% (75) of energy consumed by the brain, although later estimates found neuronal share exaggerated (21).”
The return of metabolism: biochemistry and physiology of the pentose phosphate pathway (2014, background): “These large amounts of energy are needed for the maintenance and restoration of ionic gradients and for synaptic transmission (Attwell & Laughlin, 2001).”
Allostasis: a model of predictive regulation. (2012, positive): “Since the brain is an expensive organ [19], it is efficient to coordinate and execute broad patterns at the lowest possible level, saving space and energy for the more difficult computations which can then be used to modulate lower level routines.”
Deep Sparse Rectifier Neural Networks (2011, background): “• Studies on brain energy expense suggest that neurons encode information in a sparse and distributed way (Attwell and Laughlin, 2001), estimating the percentage of neurons active at the same time to be between 1 and 4% (Lennie, 2003).”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, background): “Rate coding with Poissonian firing statistics (black lines) can also induce strong LTP, however, only at the price of much higher firing rates and, thus, much higher energy expenditures (Attwell and Laughlin, 2001; Laughlin and Sejnowski, 2003; Lennie, 2003)8.”
Is Sleep Essential? (2008, background): “of synaptic strength at the end of a waking day would result in higher energy consumption [92,93], larger”
Visual attention: The past 25 years (2011, background): “The metabolic cost of brain activity is largely dominated by neuronal activity, for which energy usage depends strongly on firing rates (Attwell & Laughlin, 2001).”
The blood-brain barrier: an engineering perspective (2013, background): “Most of the energy consumed by ATP hydrolysis in the brain is used by neurons for generating nerve impulses (e.g., voltageand ligand-gated ion channels) and for maintaining ion gradients (e.g., sodium/potassium pumps; Attwell and Laughlin, 2001; Shulman et al., 2004; Raichle and Mintun, 2006).”
13 reasons why the brain is susceptible to oxidative stress (2018, background): “64 × 10(5) ATP per second per vesicle [104,105].”
Synaptic representation of locomotion in single cerebellar granule cells (2015, background): “Since granule cells represent the most 411 abundant neuronal type in the brain, and spikes are energetically expensive 412 (Attwell and Laughlin, 2001; Carter and Bean, 2009), the metabolic cost of 413 sustained firing in this population would be considerable (Howarth et al.”