Computational role of exploration noise in error-based de novo motor learning
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Computational role of exploration noise in error-based de novo motor learning
Authors
Keywords
-
Journal
NEURAL NETWORKS
Volume 153, Issue -, Pages 349-372
Publisher
Elsevier BV
Online
2022-06-17
DOI
10.1016/j.neunet.2022.06.011
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Did we get sensorimotor adaptation wrong? Implicit adaptation as direct policy updating rather than forward-model-based learning
- (2021) Alkis M. Hadjiosif et al. JOURNAL OF NEUROSCIENCE
- De novo learning versus adaptation of continuous control in a manual tracking task
- (2021) Christopher S Yang et al. eLife
- Task-relevant and task-irrelevant variability causally shape error-based motor learning
- (2021) Lucas Rebelo Dal’Bello et al. NEURAL NETWORKS
- When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance
- (2020) Victor R. Barradas et al. JOURNAL OF NEUROPHYSIOLOGY
- Backpropagation and the brain
- (2020) Timothy P. Lillicrap et al. NATURE REVIEWS NEUROSCIENCE
- Goal-related feedback guides motor exploration and redundancy resolution in human motor skill acquisition
- (2019) Marieke Rohde et al. PLoS Computational Biology
- Rapid Plasticity of Higher-Order Thalamocortical Inputs during Sensory Learning
- (2019) Nicholas J. Audette et al. NEURON
- The dynamics of motor learning through the formation of internal models
- (2019) Camilla Pierella et al. PLoS Computational Biology
- Constraints on neural redundancy
- (2018) Jay A Hennig et al. eLife
- Using noise to shape motor learning
- (2017) Elias B. Thorp et al. JOURNAL OF NEUROPHYSIOLOGY
- Autonomous exploration of motor skills by skill babbling
- (2016) René Felix Reinhart AUTONOMOUS ROBOTS
- Thalamocortical Projections onto Behaviorally Relevant Neurons Exhibit Plasticity during Adult Motor Learning
- (2016) Jeremy S. Biane et al. NEURON
- Error Signals in Motor Cortices Drive Adaptation in Reaching
- (2016) Masato Inoue et al. NEURON
- Impact of online visual feedback on motor acquisition and retention when learning to reach in a force field
- (2016) C.S. Batcho et al. NEUROSCIENCE
- How does the brain solve muscle redundancy? Filling the gap between optimization and muscle synergy hypotheses
- (2016) Masaya Hirashima et al. NEUROSCIENCE RESEARCH
- Exploration of joint redundancy but not task space variability facilitates supervised motor learning
- (2016) Puneet Singh et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Making brain–machine interfaces robust to future neural variability
- (2016) David Sussillo et al. Nature Communications
- Random synaptic feedback weights support error backpropagation for deep learning
- (2016) Timothy P. Lillicrap et al. Nature Communications
- Cortico-Cerebellar Structural Connectivity Is Related to Residual Motor Output in Chronic Stroke
- (2015) Robert Schulz et al. CEREBRAL CORTEX
- Learning feedback and feedforward control in a mirror-reversed visual environment
- (2015) Shoko Kasuga et al. JOURNAL OF NEUROPHYSIOLOGY
- Reward-Dependent Modulation of Movement Variability
- (2015) S. E. Pekny et al. JOURNAL OF NEUROSCIENCE
- Mirror Reversal and Visual Rotation Are Learned and Consolidated via Separate Mechanisms: Recalibrating or LearningDe Novo?
- (2014) Sebastian Telgen et al. JOURNAL OF NEUROSCIENCE
- Neural constraints on learning
- (2014) Patrick T. Sadtler et al. NATURE
- Temporal structure of motor variability is dynamically regulated and predicts motor learning ability
- (2014) Howard G Wu et al. NATURE NEUROSCIENCE
- The effect of Parkinson's disease and Huntington's disease on human visuomotor learning
- (2013) Juan Manuel Gutierrez-Garralda et al. EUROPEAN JOURNAL OF NEUROSCIENCE
- Differences in Adaptation Rates after Virtual Surgeries Provide Direct Evidence for Modularity
- (2013) D. J. Berger et al. JOURNAL OF NEUROSCIENCE
- Explorative learning of inverse models: A theoretical perspective
- (2013) Matthias Rolf et al. NEUROCOMPUTING
- Thalamocortical Inputs Show Post-Critical-Period Plasticity
- (2012) Xin Yu et al. NEURON
- Learning from Sensory and Reward Prediction Errors during Motor Adaptation
- (2011) Jun Izawa et al. PLoS Computational Biology
- Error Correction, Sensory Prediction, and Adaptation in Motor Control
- (2010) Reza Shadmehr et al. Annual Review of Neuroscience
- Reorganization of Finger Coordination Patterns During Adaptation to Rotation and Scaling of a Newly Learned Sensorimotor Transformation
- (2010) Xiaolin Liu et al. JOURNAL OF NEUROPHYSIOLOGY
- Movement Intention After Parietal Cortex Stimulation in Humans
- (2009) M. Desmurget et al. SCIENCE
- Sensitivity Derivatives for Flexible Sensorimotor Learning
- (2008) M. N. Abdelghani et al. NEURAL COMPUTATION
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started