Cargando…
Dynamical differential covariance recovers directional network structure in multiscale neural systems
Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical d...
Autores principales: | Chen, Yusi, Rosen, Burke Q., Sejnowski, Terrence J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214501/ https://www.ncbi.nlm.nih.gov/pubmed/35679342 http://dx.doi.org/10.1073/pnas.2117234119 |
Ejemplares similares
-
Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times
por: Chen, Yusi, et al.
Publicado: (2022) -
Multiscale modeling of presynaptic dynamics from molecular to mesoscale
por: Garcia, Jonathan W., et al.
Publicado: (2022) -
Multiscale neural signatures of major depressive, anxiety, and stress-related disorders
por: Zhukovsky, Peter, et al.
Publicado: (2022) -
Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics
por: Sobczak, Filip, et al.
Publicado: (2020) -
Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models
por: Li, Yinghao, et al.
Publicado: (2021)