Cargando…
Mapping differential responses to cognitive training using machine learning
We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self‐organizing maps (SOMs)—a type of simple artificial neural network—to represent multivariate cognitive training data...
Autores principales: | Rennie, Joseph P., Zhang, Mengya, Hawkins, Erin, Bathelt, Joe, Astle, Duncan E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314597/ https://www.ncbi.nlm.nih.gov/pubmed/31125497 http://dx.doi.org/10.1111/desc.12868 |
Ejemplares similares
-
The cingulum as a marker of individual differences in neurocognitive development
por: Bathelt, Joe, et al.
Publicado: (2019) -
Just a phase? Mapping the transition of behavioural problems from childhood to adolescence
por: Bathelt, Joe, et al.
Publicado: (2021) -
Remapping the cognitive and neural profiles of children who struggle at school
por: Astle, Duncan E., et al.
Publicado: (2019) -
Transdiagnostic Brain Mapping in Developmental Disorders
por: Siugzdaite, Roma, et al.
Publicado: (2020) -
Introduction to Machine Learning, Neural Networks, and Deep Learning
por: Choi, Rene Y., et al.
Publicado: (2020)