Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model.
Abstract
Despite the widespread use of EEGs to measure the large-scale dynamics of the human
brain, little is known on how the dynamics of EEGs relates to that of the underlying
spike rates of cortical neurons. However, progress was made by recent neurophysiological
experiments reporting that EEG delta-band phase and gamma-band amplitude reliably
predict some complementary aspects of the time course of spikes of visual cortical
neurons. To elucidate the mechanisms behind these findings, here we hypothesize that
the EEG delta phase reflects shifts of local cortical excitability arising from slow
fluctuations in the network input due to entrainment to sensory stimuli or to fluctuations
in ongoing activity, and that the resulting local excitability fluctuations modulate
both the spike rate and the engagement of excitatory-inhibitory loops producing gamma-band
oscillations. We quantitatively tested these hypotheses by simulating a recurrent
network of excitatory and inhibitory neurons stimulated with dynamic inputs presenting
temporal regularities similar to that of thalamic responses during naturalistic visual
stimulation and during spontaneous activity. The network model reproduced in detail
the experimental relationships between spike rate and EEGs, and suggested that the
complementariness of the prediction of spike rates obtained from EEG delta phase or
gamma amplitude arises from nonlinearities in the engagement of excitatory-inhibitory
loops and from temporal modulations in the amplitude of the network input, which respectively
limit the predictability of spike rates from gamma amplitude or delta phase alone.
The model suggested also ways to improve and extend current algorithms for online
prediction of spike rates from EEGs.
Type
Journal articleSubject
Visual CortexNeurons
Animals
Macaca
Electroencephalography
Action Potentials
Models, Neurological
Neural Networks, Computer
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https://hdl.handle.net/10161/23364Published Version (Please cite this version)
10.1016/j.neuroimage.2009.12.040Publication Info
Mazzoni, Alberto; Whittingstall, Kevin; Brunel, Nicolas; Logothetis, Nikos K; & Panzeri,
Stefano (2010). Understanding the relationships between spike rate and delta/gamma frequency bands
of LFPs and EEGs using a local cortical network model. NeuroImage, 52(3). pp. 956-972. 10.1016/j.neuroimage.2009.12.040. Retrieved from https://hdl.handle.net/10161/23364.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Nicolas Brunel
Duke School of Medicine Distinguished Professor in Neuroscience
We use theoretical models of brain systems to investigate how they process and learn
information from their inputs. Our current work focuses on the mechanisms of learning
and memory, from the synapse to the network level, in collaboration with various experimental
groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity
of a network that maximizes storage capacity reproducestwo key experimentally observed
features: low connection proba

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