Cargando…
Dynamic Neural Fields with Intrinsic Plasticity
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embe...
Autores principales: | Strub, Claudius, Schöner, Gregor, Wörgötter, Florentin, Sandamirskaya, Yulia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583149/ https://www.ncbi.nlm.nih.gov/pubmed/28912706 http://dx.doi.org/10.3389/fncom.2017.00074 |
Ejemplares similares
-
Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement
por: Tekülve, Jan, et al.
Publicado: (2019) -
Dynamic neural fields as a step toward cognitive neuromorphic architectures
por: Sandamirskaya, Yulia
Publicado: (2014) -
Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields
por: Kreiser, Raphaela, et al.
Publicado: (2018) -
Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot
por: Grinke, Eduard, et al.
Publicado: (2015) -
Neural and Response Correlations to Complex Natural Sounds in the Auditory Midbrain
por: Lyzwa, Dominika, et al.
Publicado: (2016)