Literature DB >> 19888288

The energy use associated with neural computation in the cerebellum.

Clare Howarth1, Claire M Peppiatt-Wildman, David Attwell.   

Abstract

The brain's energy supply determines its information processing power, and generates functional imaging signals, which are often assumed to reflect principal neuron spiking. Using measured cellular properties, we analysed how energy expenditure relates to neural computation in the cerebellar cortex. Most energy is used on information processing by non-principal neurons: Purkinje cells use only 18% of the signalling energy. Excitatory neurons use 73% and inhibitory neurons 27% of the energy. Despite markedly different computational architectures, the granular and molecular layers consume approximately the same energy. The blood vessel area supplying glucose and O(2) is spatially matched to energy consumption. The energy cost of storing motor information in the cerebellum was also estimated.

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Year:  2009        PMID: 19888288      PMCID: PMC2859342          DOI: 10.1038/jcbfm.2009.231

Source DB:  PubMed          Journal:  J Cereb Blood Flow Metab        ISSN: 0271-678X            Impact factor:   6.200


  43 in total

Review 1.  Neuroenergetics and the kinetic design of excitatory synapses.

Authors:  David Attwell; Alasdair Gibb
Journal:  Nat Rev Neurosci       Date:  2005-11       Impact factor: 34.870

2.  Hemodynamic signals correlate tightly with synchronized gamma oscillations.

Authors:  Jörn Niessing; Boris Ebisch; Kerstin E Schmidt; Michael Niessing; Wolf Singer; Ralf A W Galuske
Journal:  Science       Date:  2005-08-05       Impact factor: 47.728

3.  Properties of somatosensory synaptic integration in cerebellar granule cells in vivo.

Authors:  Henrik Jörntell; Carl-Fredrik Ekerot
Journal:  J Neurosci       Date:  2006-11-08       Impact factor: 6.167

4.  Combined analog and action potential coding in hippocampal mossy fibers.

Authors:  Henrik Alle; Jörg R P Geiger
Journal:  Science       Date:  2006-03-03       Impact factor: 47.728

5.  Cerebellar Golgi cells in the rat: receptive fields and timing of responses to facial stimulation.

Authors:  B P Vos; A Volny-Luraghi; E De Schutter
Journal:  Eur J Neurosci       Date:  1999-08       Impact factor: 3.386

6.  Synchronization of golgi and granule cell firing in a detailed network model of the cerebellar granule cell layer.

Authors:  R Maex; E De Schutter
Journal:  J Neurophysiol       Date:  1998-11       Impact factor: 2.714

7.  Patterns of spontaneous purkinje cell complex spike activity in the awake rat.

Authors:  E J Lang; I Sugihara; J P Welsh; R Llinás
Journal:  J Neurosci       Date:  1999-04-01       Impact factor: 6.167

8.  Relative contributions of axonal and somatic Na channels to action potential initiation in cerebellar Purkinje neurons.

Authors:  Zayd M Khaliq; Indira M Raman
Journal:  J Neurosci       Date:  2006-02-15       Impact factor: 6.167

9.  Cerebellar LTD and pattern recognition by Purkinje cells.

Authors:  Volker Steuber; Wolfgang Mittmann; Freek E Hoebeek; R Angus Silver; Chris I De Zeeuw; Michael Häusser; Erik De Schutter
Journal:  Neuron       Date:  2007-04-05       Impact factor: 17.173

10.  Fly photoreceptors demonstrate energy-information trade-offs in neural coding.

Authors:  Jeremy E Niven; John C Anderson; Simon B Laughlin
Journal:  PLoS Biol       Date:  2007-04       Impact factor: 8.029

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  40 in total

Review 1.  Updated energy budgets for neural computation in the neocortex and cerebellum.

Authors:  Clare Howarth; Padraig Gleeson; David Attwell
Journal:  J Cereb Blood Flow Metab       Date:  2012-03-21       Impact factor: 6.200

2.  Scaling of neural responses to visual and auditory motion in the human cerebellum.

Authors:  Oliver Baumann; Jason B Mattingley
Journal:  J Neurosci       Date:  2010-03-24       Impact factor: 6.167

Review 3.  Universal Transform or Multiple Functionality? Understanding the Contribution of the Human Cerebellum across Task Domains.

Authors:  Jörn Diedrichsen; Maedbh King; Carlos Hernandez-Castillo; Marty Sereno; Richard B Ivry
Journal:  Neuron       Date:  2019-06-05       Impact factor: 17.173

4.  Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

Authors:  Dardo G Tomasi; Ehsan Shokri-Kojori; Corinde E Wiers; Sunny W Kim; Şukru B Demiral; Elizabeth A Cabrera; Elsa Lindgren; Gregg Miller; Gene-Jack Wang; Nora D Volkow
Journal:  J Cereb Blood Flow Metab       Date:  2017-05-23       Impact factor: 6.200

5.  Energy demand of synaptic transmission at the hippocampal Schaffer-collateral synapse.

Authors:  Agustin Liotta; Jörg Rösner; Christine Huchzermeyer; Anna Wojtowicz; Oliver Kann; Dietmar Schmitz; Uwe Heinemann; Richard Kovács
Journal:  J Cereb Blood Flow Metab       Date:  2012-08-29       Impact factor: 6.200

6.  Cross inhibition from ON to OFF pathway improves the efficiency of contrast encoding in the mammalian retina.

Authors:  Zhiyin Liang; Michael A Freed
Journal:  J Neurophysiol       Date:  2012-08-29       Impact factor: 2.714

7.  Cerebellar activation related to saccadic inaccuracies.

Authors:  Esmee I M L Liem; Maarten A Frens; Marion Smits; Jos N van der Geest
Journal:  Cerebellum       Date:  2013-04       Impact factor: 3.847

8.  Brain active transmembrane water cycling measured by MR is associated with neuronal activity.

Authors:  Ruiliang Bai; Charles S Springer; Dietmar Plenz; Peter J Basser
Journal:  Magn Reson Med       Date:  2018-09-08       Impact factor: 4.668

9.  The energetics of CNS white matter.

Authors:  Julia J Harris; David Attwell
Journal:  J Neurosci       Date:  2012-01-04       Impact factor: 6.167

10.  Linearization of excitatory synaptic integration at no extra cost.

Authors:  Danielle Morel; Chandan Singh; William B Levy
Journal:  J Comput Neurosci       Date:  2018-01-25       Impact factor: 1.621

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