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Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control

Overview of attention for article published in Frontiers in Neural Circuits, October 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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1 blog
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8 X users

Citations

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23 Dimensions

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94 Mendeley
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Title
Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control
Published in
Frontiers in Neural Circuits, October 2014
DOI 10.3389/fncir.2014.00126
Pubmed ID
Authors

Sakyasingha Dasgupta, Florentin Wörgötter, Poramate Manoonpong

Abstract

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
France 2 2%
United States 2 2%
United Kingdom 2 2%
Portugal 1 1%
Spain 1 1%
Canada 1 1%
Unknown 83 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 22%
Researcher 16 17%
Student > Master 12 13%
Professor 8 9%
Student > Doctoral Student 7 7%
Other 17 18%
Unknown 13 14%
Readers by discipline Count As %
Neuroscience 20 21%
Computer Science 14 15%
Psychology 13 14%
Agricultural and Biological Sciences 11 12%
Engineering 5 5%
Other 14 15%
Unknown 17 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 10 April 2015.
All research outputs
#3,145,106
of 25,759,158 outputs
Outputs from Frontiers in Neural Circuits
#160
of 1,302 outputs
Outputs of similar age
#35,036
of 274,985 outputs
Outputs of similar age from Frontiers in Neural Circuits
#2
of 18 outputs
Altmetric has tracked 25,759,158 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,302 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done well, scoring higher than 87% 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 274,985 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.