Cargando…
Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation
The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven...
Autores principales: | Luque, Niceto R., Garrido, Jesús A., Carrillo, Richard R., D'Angelo, Egidio, Ros, Eduardo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133770/ https://www.ncbi.nlm.nih.gov/pubmed/25177290 http://dx.doi.org/10.3389/fncom.2014.00097 |
Ejemplares similares
-
Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation
por: Garrido, Jesús A., et al.
Publicado: (2013) -
Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model
por: Luque, Niceto R., et al.
Publicado: (2016) -
Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network
por: Casellato, Claudia, et al.
Publicado: (2014) -
Connection control implications in a distributed plasticity cerebellar model
por: Luque, Niceto R, et al.
Publicado: (2013) -
Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue
por: D’Angelo, Egidio, et al.
Publicado: (2016)