Cargando…
Modeling time-varying brain networks with a self-tuning optimized Kalman filter
Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain...
Autores principales: | Pascucci, D., Rubega, M., Plomp, G. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451990/ https://www.ncbi.nlm.nih.gov/pubmed/32804971 http://dx.doi.org/10.1371/journal.pcbi.1007566 |
Ejemplares similares
-
A Kalman-Filter Based Approach to Identification of Time-Varying Gene Regulatory Networks
por: Xiong, Jie, et al.
Publicado: (2013) -
Editorial: Chasing brain dynamics at their speed: what can time-varying functional connectivity tell us about brain function?
por: Rubega, Maria, et al.
Publicado: (2023) -
Kalman filtering and neural networks
por: Haykin, Simon S
Publicado: (2001) -
Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance
por: Ge, Baoshuang, et al.
Publicado: (2019) -
Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter
por: Yang, Ning, et al.
Publicado: (2022)