Brain signal predictions from multi-scale networks using a linearized framework.

ABSTRACT: Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ('spikes') or spik

SUBMITTER: Hagen E 

PROVIDER: S-EPMC9401172 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature