Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance

(2023) Taisei Sugiyama et al.   Nature Communications

Interactions between sensory prediction error and task error during implicit motor learning

(2022) Jonathan S. Tsay et al.   PLoS Computational Biology

Computational role of exploration noise in error-based de novo motor learning

(2022) Lucas Rebelo Dal’Bello et al.   NEURAL NETWORKS

Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes

(2021) Guy Avraham et al.   PLOS BIOLOGY

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

Both Fast and Slow Learning Processes Contribute to Savings Following Sensorimotor Adaptation

(2019) Susan K Coltman et al.   JOURNAL OF NEUROPHYSIOLOGY

A deep learning framework for neuroscience

(2019) Blake A. Richards et al.   NATURE NEUROSCIENCE

The dynamics of motor learning through the formation of internal models

(2019) Camilla Pierella et al.   PLoS Computational Biology

Corticospinal correlates of fast and slow adaptive processes in motor learning

(2018) Adjmal M.E. Sarwary et al.   JOURNAL OF NEUROPHYSIOLOGY

Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

(2018) Shota Hagio et al.   Journal of the Royal Society Interface

Toward an Integration of Deep Learning and Neuroscience

(2016) Adam H. Marblestone et al.   Frontiers in Computational Neuroscience

Questioning the role of sparse coding in the brain

(2015) Anton Spanne et al.   TRENDS IN NEUROSCIENCES

Internal models for interpreting neural population activity during sensorimotor control

(2015) Matthew D Golub et al.   eLife

nparLD: AnRSoftware Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

(2015) Kimihiro Noguchi et al.   Journal of Statistical Software

A memory of errors in sensorimotor learning

(2014) D. J. Herzfeld et al.   SCIENCE

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

Encoding of Sensory Prediction Errors in the Human Cerebellum

(2012) J. Schlerf et al.   JOURNAL OF NEUROSCIENCE

Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control

(2011) Mitsuo Kawato et al.   CURRENT OPINION IN NEUROBIOLOGY

Motor Memory and Local Minimization of Error and Effort, Not Global Optimization, Determine Motor Behavior

(2010) G. Ganesh et al.   JOURNAL OF NEUROPHYSIOLOGY

Dual Adaptation Supports a Parallel Architecture of Motor Memory

(2009) J.-Y. Lee et al.   JOURNAL OF NEUROSCIENCE