Structural Stability within the Lateral Cerebellar Nucleus of the Rat Following Complex Motor Learning

SUMMARYThe contribution of cerebellum to motor learning is often considered to be limited to adaptation, a short-timescale tuning of reflexes and previous learned skills. Yet, the cerebellum is reciprocally connected to two main players of motor learning, the motor cortex and the basal ganglia, via the ventral and midline thalamus respectively. Here, we evaluated the contribution of cerebellar neurons projecting to these thalamic nuclei in a skilled locomotion task in mice. In the cerebellar nuclei, we found task-specific neuronal activities during the task, and lasting changes after the task suggesting an offline processing of task-related information. Using pathway-specific inhibition, we found that dentate neurons projecting to the midline thalamus contribute to learning and retrieval, while interposed neurons projecting to the ventral thalamus contribute to the offline consolidation of savings. Our results thus show that two parallel cerebello-thalamic pathways perform distinct computations operating on distinct timescales in motor learning.

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Participation can make a difference to be competitive in sports: A systematic review on the relation between complex motor development and self-controlled learning settings

International Journal of Sports Science & Coaching ◽

10.1177/1747954118825063 ◽

2019 ◽

Vol 14 (2) ◽

pp. 255-269

Author(s):

David Jaitner ◽

Filip Mess

Keyword(s):

Motor Learning ◽

Motor Development ◽

Full Text ◽

Self Control ◽

Competitive Sports ◽

Target Groups ◽

The Social ◽

Complex Motor ◽

The Status ◽

And Performance

The purpose of the study was to provide an empirically based argument that grounds the relation between potentials of athletic performance and participatory settings within the autonomous inner logic of competitive sports. Therefore, the present paper systematically reviewed the empirical evidence of the association between complex motor learning and performance, and self-controlled practice conditions. Six electronic databases, reference lists and citations of full-text articles were searched for English and German language peer-reviewed articles. The search string multiply combined different terms relating to motor learning AND self-control. Two reviewers evaluated the full-text articles and critically appraised the included studies. Thirty-one studies with 1273 participants met the inclusion criteria. The vast majority of the studies reported significant learning advantages for experimental groups under self-controlled practice conditions compared to experimenter-imposed yoked groups. No study showed adverse effects. Thereby, the effects of self-controlled practice conditions have been shown to be relatively generalised to a variety of participatory variables and target groups. Advantages in accuracy, form and performance were more frequently reported than advantages in consistency. Despite increasing research efforts, the explanatory underpinnings behind the learning benefits remain debatable. The evidence indicates that complex motor learning and motor performance are typically enhanced when learners are given the opportunity to take part in decisions and therefore presents profitable implications for coaches and anyone responsible in competitive sports. However, in order to become a professional effective argument, the social context and the status of reasoning in changing habits need to be considered.

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Changes in Cerebral Hemodynamics during Complex Motor Learning by Character Entry into Touch-Screen Terminals

PLoS ONE ◽

10.1371/journal.pone.0140552 ◽

2015 ◽

Vol 10 (10) ◽

pp. e0140552 ◽

Cited By ~ 5

Author(s):

Akira Sagari ◽

Naoki Iso ◽

Takefumi Moriuchi ◽

Kakuya Ogahara ◽

Eiji Kitajima ◽

...

Keyword(s):

Motor Learning ◽

Touch Screen ◽

Cerebral Hemodynamics ◽

Complex Motor

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A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity

Neural Computation ◽

10.1162/neco_a_01198 ◽

2019 ◽

Vol 31 (7) ◽

pp. 1430-1461 ◽

Cited By ~ 2

Author(s):

Ryan Pyle ◽

Robert Rosenbaum

Keyword(s):

Motor Learning ◽

Learning Algorithm ◽

Motor Task ◽

Reservoir Computing ◽

Motor Tasks ◽

Biologically Inspired ◽

Target Time ◽

Learning Rules ◽

Complex Motor ◽

Experimental Findings

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.

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Rehabilitation Training Using Complex Motor Learning Rescues Deficits in Eyeblink Classical Conditioning in Female Rats Induced by Binge-Like Neonatal Alcohol Exposure

Alcoholism Clinical and Experimental Research ◽

10.1111/acer.12122 ◽

2013 ◽

Vol 37 (9) ◽

pp. 1561-1570 ◽

Cited By ~ 9

Author(s):

Jennifer L. Wagner ◽

Anna Y. Klintsova ◽

William T. Greenough ◽

Charles R. Goodlett

Keyword(s):

Motor Learning ◽

Classical Conditioning ◽

Alcohol Exposure ◽

Female Rats ◽

Rehabilitation Training ◽

Complex Motor ◽

Neonatal Alcohol Exposure

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Failure of Motor Learning for Large Initial Errors

Neural Computation ◽

10.1162/0899766041336431 ◽

2004 ◽

Vol 16 (9) ◽

pp. 1873-1886 ◽

Cited By ~ 41

Author(s):

Terence D. Sanger

Keyword(s):

Motor Learning ◽

Failure Modes ◽

Learning Rule ◽

Motor Tasks ◽

Mathematical Basis ◽

Initial Errors ◽

Repeated Practice ◽

Complex Motor ◽

Human Skills ◽

Training Examples

For certain complex motor tasks, humans may experience the frustration of a lack of improvement despite repeated practice. We investigate a computational basis for failure of motor learning when there is no prior information about the system to be controlled and when it is not practical to perform a thorough random exploration of the set of possible commands. In this case, if the desired movement has never yet been performed, then it may not be possible to learn the correct motor commands since there will be no appropriate training examples. We derive the mathematical basis for this phenomenon when the controller can be modeled as a linear combination of nonlinear basis functions trained using a gradient descent learning rule on the observed commands and their results. We show that there are two failure modes for which continued training examples will never lead to improvement in performance. We suggest that this may provide a model for the lack of improvement in human skills that can occur despite repeated practice of a complex task.

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Online Movement Monitoring Modulates Feedback Processing in Motor Learning: An Analysis of Event-Related Potentials

Journal of Motor Learning and Development ◽

10.1123/jmld.2016-0075 ◽

2018 ◽

Vol 6 (s1) ◽

pp. S138-S153 ◽

Cited By ~ 1

Author(s):

Michael Joch ◽

Mathias Hegele ◽

Heiko Maurer ◽

Hermann Müller ◽

Lisa K. Maurer

Keyword(s):

Motor Learning ◽

Motor Task ◽

Event Related Potentials ◽

Neural Correlates ◽

Difficulty Level ◽

Error Processing ◽

Error Prediction ◽

Feedback Processing ◽

Complex Motor ◽

Related Potentials

Motor learning can be monitored by observing the development of neural correlates of error processing. Among these neural correlates, the error- and feedback-related negativity (Ne/ERN and FRN) represent error processing mechanisms. While the Ne/ERN is more related to error prediction, the FRN is found after an error is manifested. The questions the current study strives to answer are: What information is needed by the system to make error predictions and how is this represented by the Ne/ERN and FRN in a complex motor task? We reduced the information and increased the difficulty level for the prediction in a semivirtual throwing task and found no Ne/ERN but a large FRN when the action result was finally observed (hitting or missing a target). We assume that uncertainty for error prediction was too high (either due to insufficient information or due to lacking prerequisites for prediction), such that error processing had to be mainly based on feedback. The finding is in line with the reinforcement theory of learning, after which Ne/ERN and FRN should behave complementary.

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