Cargando…
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that a...
Autores principales: | Kutschireiter, Anna, Surace, Simone Carlo, Sprekeler, Henning, Pfister, Jean-Pascal |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562918/ https://www.ncbi.nlm.nih.gov/pubmed/28821729 http://dx.doi.org/10.1038/s41598-017-06519-y |
Ejemplares similares
-
Publisher Correction: Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception
por: Kutschireiter, Anna, et al.
Publicado: (2017) -
Approximate nonlinear filtering with a recurrent neural network
por: Kutschireiter, Anna, et al.
Publicado: (2015) -
Learning as filtering: Implications for spike-based plasticity
por: Jegminat, Jannes, et al.
Publicado: (2022) -
A Statistical Model for In Vivo Neuronal Dynamics
por: Surace, Simone Carlo, et al.
Publicado: (2015) -
Efficient sampling-based Bayesian Active Learning for synaptic characterization
por: Gontier, Camille, et al.
Publicado: (2023)