Cargando…
The number of granular cells in a cerebellar neuronal network model engaged during robot control increases with the complexity of the motor task
Autores principales: | Pinzon-Morales, Ruben-Dario, Hirata, Yutaka |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125049/ http://dx.doi.org/10.1186/1471-2202-15-S1-P143 |
Ejemplares similares
-
Adaptive control of 2-wheeled balancing robot by two hemispheric cerebellar neuronal network model
por: Pinzon-Morales, Ruben-Dario, et al.
Publicado: (2012) -
Spontaneous firing activity in climbing fiber is critical for a realistic bi-hemispherical cerebellar neuronal network during robot control
por: Pinzon-Morales, Ruben-Dario, et al.
Publicado: (2014) -
A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control
por: Pinzon-Morales, Ruben-Dario, et al.
Publicado: (2014) -
The origin of the frequency selectivity in VOR motor learning revealed by a realistic cerebellar spiking neuron network model.
por: Inagaki, Keiichiro, et al.
Publicado: (2010) -
Evaluation of Teaching Signals for Motor Control in the Cerebellum during Real-World Robot Application
por: Pinzon Morales, Ruben Dario, et al.
Publicado: (2016)