Computation of the electroencephalogram (EEG) from network models of point neurons.

ABSTRACT: 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-fi

SUBMITTER: Martinez-Canada P 

PROVIDER: S-EPMC8046357 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature