Literature DB >> 31642401

The asynchronous state's relation to large-scale potentials in cortex.

A Alishbayli1,2, J G Tichelaar1,3, U Gorska1,4,5, M X Cohen6,3, B Englitz1.   

Abstract

Understanding the relation between large-scale potentials (M/EEG) and their underlying neural activity can improve the precision of research and clinical diagnosis. Recent insights into cortical dynamics highlighted a state of strongly reduced spike count correlations, termed the asynchronous state (AS). The AS has received considerable attention from experimenters and theorists alike, regarding its implications for cortical dynamics and coding of information. However, how reconcilable are these vanishing correlations in the AS with large-scale potentials such as M/EEG observed in most experiments? Typically the latter are assumed to be based on underlying correlations in activity, in particular between subthreshold potentials. We survey the occurrence of the AS across brain states, regions, and layers and argue for a reconciliation of this seeming disparity: large-scale potentials are either observed, first, at transitions between cortical activity states, which entail transient changes in population firing rate, as well as during the AS, and, second, on the basis of sufficiently large, asynchronous populations that only need to exhibit weak correlations in activity. Cells with no or little spiking activity can contribute to large-scale potentials via their subthreshold currents, while they do not contribute to the estimation of spiking correlations, defining the AS. Furthermore, third, the AS occurs only within particular cortical regions and layers associated with the currently selected modality, allowing for correlations at other times and between other areas and layers.

Keywords:  EEG; MEG; cognitive state; correlations; population activity

Year:  2019        PMID: 31642401      PMCID: PMC6966315          DOI: 10.1152/jn.00013.2019

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  129 in total

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4.  Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

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Journal:  Nat Rev Neurosci       Date:  2011-08-10       Impact factor: 34.870

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