Evidence map›Paper›PMID 23801941›Full record

ArticleFrontiers in neural circuits2013

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

A Hanuschkin, S Ganguli, R H R Hahnloser

Abstract read
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Article in Frontiers in neural circuits, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Building a state space for song learning.Current opinion in neurobiology · 2018
    Review
  9. Article
  10. Article
  11. Toward an Integration of Deep Learning and Neuroscience.Frontiers in computational neuroscience · 2016
    Article
  12. A Comprehensive Account of Sound Sequence Imitation in the Songbird.Frontiers in computational neuroscience · 2016
    Article
  13. A Duet for one.Consciousness and cognition · 2015
    Review
  14. Article
  15. Article
  16. Review
  17. Evidence for a causal inverse model in an avian cortico-basal ganglia circuit.Proceedings of the National Academy of Sciences of the United States of America · 2014
    Article
  18. Article
  19. Article
  20. Closed-loop neuroscience and neuroengineering.Frontiers in neural circuits · 2014
    Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

A HanuschkinInstitute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland ; Neuroscience Center Zurich (ZNZ) Zurich, Switzerland.
S Ganguli
R H R Hahnloser

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

LearningMirror NeuronsModels, NeurologicalVocalization, AnimalAnimalsAuditory PerceptionSongbirdsinverse problemlinear modelsmirror neuronssensory motor learningsongbird

Identifiers

PMID23801941
PMCPMC3686052

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.