↓ Skip to main content

Reward Based Motor Adaptation Mediated by Basal Ganglia

Overview of attention for article published in Frontiers in Computational Neuroscience, March 2017
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
8 X users

Readers on

mendeley
92 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Reward Based Motor Adaptation Mediated by Basal Ganglia
Published in
Frontiers in Computational Neuroscience, March 2017
DOI 10.3389/fncom.2017.00019
Pubmed ID
Authors

Taegyo Kim, Khaldoun C. Hamade, Dmitry Todorov, William H. Barnett, Robert A. Capps, Elizaveta M. Latash, Sergey N. Markin, Ilya A. Rybak, Yaroslav I. Molkov

Abstract

It is widely accepted that the basal ganglia (BG) play a key role in action selection and reinforcement learning. However, despite considerable number of studies, the BG architecture and function are not completely understood. Action selection and reinforcement learning are facilitated by the activity of dopaminergic neurons, which encode reward prediction errors when reward outcomes are higher or lower than expected. The BG are thought to select proper motor responses by gating appropriate actions, and suppressing inappropriate ones. The direct striato-nigral (GO) and the indirect striato-pallidal (NOGO) pathways have been suggested to provide the functions of BG in the two-pathway concept. Previous models confirmed the idea that these two pathways can mediate the behavioral choice, but only for a relatively small number of potential behaviors. Recent studies have provided new evidence of BG involvement in motor adaptation tasks, in which adaptation occurs in a non-error-based manner. In such tasks, there is a continuum of possible actions, each represented by a complex neuronal activity pattern. We extended the classical concept of the two-pathway BG by creating a model of BG interacting with a movement execution system, which allows for an arbitrary number of possible actions. The model includes sensory and premotor cortices, BG, a spinal cord network, and a virtual mechanical arm performing 2D reaching movements. The arm is composed of 2 joints (shoulder and elbow) controlled by 6 muscles (4 mono-articular and 2 bi-articular). The spinal cord network contains motoneurons, controlling the muscles, and sensory interneurons that receive afferent feedback and mediate basic reflexes. Given a specific goal-oriented motor task, the BG network through reinforcement learning constructs a behavior from an arbitrary number of basic actions represented by cortical activity patterns. Our study confirms that, with slight modifications, the classical two-pathway BG concept is consistent with results of previous studies, including non-error based motor adaptation experiments, pharmacological manipulations with BG nuclei, and functional deficits observed in BG-related motor disorders.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Unknown 90 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 27%
Student > Master 14 15%
Researcher 11 12%
Student > Doctoral Student 8 9%
Student > Bachelor 3 3%
Other 8 9%
Unknown 23 25%
Readers by discipline Count As %
Neuroscience 31 34%
Agricultural and Biological Sciences 4 4%
Engineering 4 4%
Computer Science 3 3%
Physics and Astronomy 3 3%
Other 17 18%
Unknown 30 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 November 2017.
All research outputs
#6,892,415
of 24,226,848 outputs
Outputs from Frontiers in Computational Neuroscience
#346
of 1,406 outputs
Outputs of similar age
#105,773
of 313,254 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#8
of 29 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,406 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 313,254 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.