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A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

Overview of attention for article published in Frontiers in Neural Circuits, January 2013
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  • 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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Title
A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models
Published in
Frontiers in Neural Circuits, January 2013
DOI 10.3389/fncir.2013.00106
Pubmed ID
Authors

A. Hanuschkin, S. Ganguli, R. H. R. Hahnloser

Abstract

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Switzerland 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
Finland 1 <1%
Denmark 1 <1%
New Zealand 1 <1%
Unknown 136 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 25%
Student > Ph. D. Student 34 23%
Student > Master 16 11%
Student > Bachelor 16 11%
Professor 10 7%
Other 19 13%
Unknown 15 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 20%
Neuroscience 27 18%
Computer Science 16 11%
Psychology 15 10%
Engineering 12 8%
Other 28 19%
Unknown 19 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 30 July 2021.
All research outputs
#3,478,101
of 24,340,143 outputs
Outputs from Frontiers in Neural Circuits
#225
of 1,266 outputs
Outputs of similar age
#35,584
of 289,409 outputs
Outputs of similar age from Frontiers in Neural Circuits
#20
of 170 outputs
Altmetric has tracked 24,340,143 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,266 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done well, scoring higher than 82% 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 289,409 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 170 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.