In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 4 ( 2021-4-2), p. e1008893-
Kurzfassung:
The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent’s EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85–95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2–8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.
Materialart:
Online-Ressource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008893
DOI:
10.1371/journal.pcbi.1008893.g001
DOI:
10.1371/journal.pcbi.1008893.g002
DOI:
10.1371/journal.pcbi.1008893.g003
DOI:
10.1371/journal.pcbi.1008893.g004
DOI:
10.1371/journal.pcbi.1008893.g005
DOI:
10.1371/journal.pcbi.1008893.g006
DOI:
10.1371/journal.pcbi.1008893.g007
DOI:
10.1371/journal.pcbi.1008893.g008
DOI:
10.1371/journal.pcbi.1008893.g009
DOI:
10.1371/journal.pcbi.1008893.g010
DOI:
10.1371/journal.pcbi.1008893.t001
DOI:
10.1371/journal.pcbi.1008893.t002
DOI:
10.1371/journal.pcbi.1008893.t003
DOI:
10.1371/journal.pcbi.1008893.t004
DOI:
10.1371/journal.pcbi.1008893.t005
DOI:
10.1371/journal.pcbi.1008893.t006
DOI:
10.1371/journal.pcbi.1008893.t007
DOI:
10.1371/journal.pcbi.1008893.s001
DOI:
10.1371/journal.pcbi.1008893.s002
DOI:
10.1371/journal.pcbi.1008893.s003
DOI:
10.1371/journal.pcbi.1008893.s004
DOI:
10.1371/journal.pcbi.1008893.s005
DOI:
10.1371/journal.pcbi.1008893.s006
DOI:
10.1371/journal.pcbi.1008893.r001
DOI:
10.1371/journal.pcbi.1008893.r002
DOI:
10.1371/journal.pcbi.1008893.r003
DOI:
10.1371/journal.pcbi.1008893.r004
DOI:
10.1371/journal.pcbi.1008893.r005
DOI:
10.1371/journal.pcbi.1008893.r006
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2021
ZDB Id:
2193340-6
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