Energy limitation as a selective pressure on the evolution of sensory systems

By Jeremy Edward Niven, Simon Barry Laughlin. Journal of Experimental Biology, 2008

1,023 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9a329ac01124b1b23a06ccaad929911d87ab9c5e

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Citations in the corpus

Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, background): “%) in proportion to the total energy budget of the body, and the largest single cost is that of ATP-dependent active transport systems required to restore neuronal membrane potentials after depolarization (Niven and Laughlin, 2008).”
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma (2021, background): “4 Synaptic Importance May Govern Energy-Information Trade-offs Energy constraints are widely believed to be a main driver of evolution (Niven and Laughlin, 2008).”
Are bigger brains better? (2009, background): “Larger neurons are less efficient than smaller neurons irrespective of the rate at which they are transmitting information [41].”
Brain graphs: graphical models of the human brain connectome. (2011, result): “…of large-scale brain networks, and it is compatible with much prior data and analysis indicating that many aspects of brain anatomical organization can be approximated by wiring minimization principles (Attwell & Laughlin 2001, Chen et al. 2006, Kaiser & Hilgetag 2006, Niven & Laughlin 2008).”
The ecological impacts of nighttime light pollution: a mechanistic appraisal (2013, background): “Indeed, considerable energetic costs are often borne in order to maintain the necessary sensory systems (Niven & Laughlin, 2008).”
Grade Changes in Brain–Body Allometry: Morphological and Behavioural Correlates of Brain Size in Miniature Spiders, Insects and Other Invertebrates (2011, background): “Theoretically we find this argument untenable because it neglects the costly expense of maintaining excess nervous tissue (Hasenstaub et al., 2010; Niven and Laughlin, 2008).”
Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation. (2010, background): “This finding suggests that, during natural vision, RSC neurons are more energetically efficient (Niven and Laughlin, 2008), more selective (Figure 1D) and reliable (Figure 4C), and consequently more informative per spike (Vinje and Gallant, 2002).”
Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, background): “Given the high metabolic costs of the brain (about 20% of the total energy budget for only 2% of body mass in the human), of which a large proportion is due to the costs of building and maintaining functional connections between anatomically distributed neurons [43,44], it seems reasonable to ask: w”
Simple models of human brain functional networks (2012, background): “…of the nematode worm Caenorhabditis elegans , which has been mapped completely at the cellular level of synaptic connections between neurons (13), most axonal projections are shorter than the average distance between neurons, as expected in response to an economical selection pressure (14, 15).”
Conserved and variable architecture of human white matter connectivity (2011, neutral): “…and Strogatz, 1998; Hilgetag et al., 2000) and physical space (Sakata et al., 2005; Kaiser and Hilgetag, 2006; Chen et al., 2006; He et al., 2007; Bassett et al., 2008) are rare, arguably due to energetic constraints (Bassett et al., 2009; Attwell and Laughlin, 2001; Niven and Laughlin, 2008).”