Cargando…
Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network
Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to in...
Autores principales: | Del Papa, Bruno, Priesemann, Viola, Triesch, Jochen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5446191/ https://www.ncbi.nlm.nih.gov/pubmed/28552964 http://dx.doi.org/10.1371/journal.pone.0178683 |
Ejemplares similares
-
SORN: A Self-Organizing Recurrent Neural Network
por: Lazar, Andreea, et al.
Publicado: (2009) -
Self-organization to sub-criticality
por: Priesemann, V
Publicado: (2015) -
Nonlinear Dynamics Analysis of a Self-Organizing Recurrent Neural Network: Chaos Waning
por: Eser, Jürgen, et al.
Publicado: (2014) -
Learning more by sampling less: subsampling effects are model specific
por: Priesemann, Viola, et al.
Publicado: (2013) -
Key features of neural variability emerge from self-organized sequence learning in a deterministic neural network
por: Hartmann, Christoph, et al.
Publicado: (2015)