相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance
Taisei Sugiyama et al.
NATURE COMMUNICATIONS (2023)
Interactions between sensory prediction error and task error during implicit motor learning
Jonathan S. Tsay et al.
PLOS COMPUTATIONAL BIOLOGY (2022)
Computational role of exploration noise in error-based de novo motor learning
Lucas Rebelo Dal'Bello et al.
NEURAL NETWORKS (2022)
Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes
Guy Avraham et al.
PLOS BIOLOGY (2021)
When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance
Victor R. Barradas et al.
JOURNAL OF NEUROPHYSIOLOGY (2020)
Backpropagation and the brain
Timothy P. Lillicrap et al.
NATURE REVIEWS NEUROSCIENCE (2020)
Both fast and slow learning processes contribute to savings following sensorimotor adaptation
Susan K. Coltman et al.
JOURNAL OF NEUROPHYSIOLOGY (2019)
A deep learning framework for neuroscience
Blake A. Richards et al.
NATURE NEUROSCIENCE (2019)
The dynamics of motor learning through the formation of internal models
Camilla Pierella et al.
PLOS COMPUTATIONAL BIOLOGY (2019)
Corticospinal correlates of fast and slow adaptive processes in motor learning
Adjmal M. E. Sarwary et al.
JOURNAL OF NEUROPHYSIOLOGY (2018)
Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry
Shota Hagio et al.
JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)
Toward an Integration of Deep Learning and Neuroscience
Adam H. Marblestone et al.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2016)
Questioning the role of sparse coding in the brain
Anton Spanne et al.
TRENDS IN NEUROSCIENCES (2015)
Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation
Sungshin Kim et al.
PLOS BIOLOGY (2015)
Internal models for interpreting neural population activity during sensorimotor control
Matthew D. Golub et al.
ELIFE (2015)
Explorative learning of inverse models: A theoretical perspective
Matthias Rolf et al.
NEUROCOMPUTING (2014)
A memory of errors in sensorimotor learning
David J. Herzfeld et al.
SCIENCE (2014)
Differences in Adaptation Rates after Virtual Surgeries Provide Direct Evidence for Modularity
Denise J. Berger et al.
JOURNAL OF NEUROSCIENCE (2013)
Encoding of Sensory Prediction Errors in the Human Cerebellum
John Schlerf et al.
JOURNAL OF NEUROSCIENCE (2012)
nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments
Kimihiro Noguchi et al.
JOURNAL OF STATISTICAL SOFTWARE (2012)
Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control
Mitsuo Kawato et al.
CURRENT OPINION IN NEUROBIOLOGY (2011)
Motor Memory and Local Minimization of Error and Effort, Not Global Optimization, Determine Motor Behavior
G. Ganesh et al.
JOURNAL OF NEUROPHYSIOLOGY (2010)
Dual Adaptation Supports a Parallel Architecture of Motor Memory
Jeong-Yoon Lee et al.
JOURNAL OF NEUROSCIENCE (2009)
Motor adaptation as a greedy optimization of error and effort
Jeremy L. Emken et al.
JOURNAL OF NEUROPHYSIOLOGY (2007)
Interacting adaptive processes with different timescales underlie short-term motor learning
Maurice A. Smith et al.
PLOS BIOLOGY (2006)
Learning curves for stochastic gradient descent in linear feedforward networks
J Werfel et al.
NEURAL COMPUTATION (2005)
Adaptation to visuomotor transformations: Consolidation, interference, and forgetting
JW Krakauer et al.
JOURNAL OF NEUROSCIENCE (2005)
Chaos may enhance information transmission in the inferior olive
N Schweighofer et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2004)
Failure of motor learning for large initial errors
TD Sanger
NEURAL COMPUTATION (2004)
Factors governing the form of the relation between muscle force and the EMG: A simulation study
P Zhou et al.
JOURNAL OF NEUROPHYSIOLOGY (2004)
Optimal feedback control as a theory of motor coordination
E Todorov et al.
NATURE NEUROSCIENCE (2002)
Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control
N Schweighofer et al.
NEUROSCIENCE (2001)
Learning of visuomotor transformations for vectorial planning of reaching trajectories
JW Krakauer et al.
JOURNAL OF NEUROSCIENCE (2000)
Learning of action through adaptive combination of motor primitives
KA Thoroughman et al.
NATURE (2000)
分享论文
