Cargando…
Deep networks for motor control functions
The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward t...
Autores principales: | Berniker, Max, Kording, Konrad P. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4365717/ https://www.ncbi.nlm.nih.gov/pubmed/25852530 http://dx.doi.org/10.3389/fncom.2015.00032 |
Ejemplares similares
-
Estimating the sources of motor errors for adaptation and generalization
por: Berniker, Max, et al.
Publicado: (2008) -
Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects
por: Berniker, Max, et al.
Publicado: (2011) -
An Examination of the Generalizability of Motor Costs
por: Berniker, Max, et al.
Publicado: (2013) -
Learning Priors for Bayesian Computations in the Nervous System
por: Berniker, Max, et al.
Publicado: (2010) -
Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning
por: Berniker, Max, et al.
Publicado: (2013)