A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Conductance-based Dynamic Causal Modeling: A mathematical review of its application to cross-power spectral densities
[article]
2021
arXiv
pre-print
Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease.
arXiv:2104.02957v1
fatcat:n2rhpi4o6jclnknlbw4rb7wpyi