Citation
A. Hanuschkin, S. Ganguli, R. H. R Hahnloser. "A Hebbian Learning Rule gives Rise to Mirror Neurons and Links Them to Control Theoretic Inverse Models". Frontiers in Neural Circuits, 2013.

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.

Electronic downloads

• http://www.ncbi.nlm.nih.gov/pubmed/23801941

• HTML

  A. Hanuschkin, S. Ganguli, R. H. R Hahnloser. <a
  href="http://robotics.eecs.berkeley.edu/pubs/7.html"
  >A Hebbian Learning Rule gives Rise to Mirror Neurons and
  Links Them to Control Theoretic Inverse Models</a>,
  <i>Frontiers in Neural Circuits</i>,  2013.

• Plain text

  A. Hanuschkin, S. Ganguli, R. H. R Hahnloser. "A
  Hebbian Learning Rule gives Rise to Mirror Neurons and Links
  Them to Control Theoretic Inverse Models".
  <i>Frontiers in Neural Circuits</i>,  2013.

• BibTeX

  @article{HanuschkinGanguliHahnloser13_HebbianLearningRuleGivesRiseToMirrorNeuronsLinksThem,
      author = {A. Hanuschkin and S. Ganguli and R. H. R Hahnloser},
      title = {A Hebbian Learning Rule gives Rise to Mirror
                Neurons and Links Them to Control Theoretic
                Inverse Models},
      journal = {Frontiers in Neural Circuits},
      year = {2013},
      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.},
      URL = {http://robotics.eecs.berkeley.edu/pubs/7.html}
  }
  

Posted by Ehsan Elhamifar on 16 May 2014.
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