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ref: -0 tags: hahnloser zebrafinch LMAN HVC song learning internal model date: 10-12-2018 00:33 gmt revision:1 [0] [head]

PMID-24711417 Evidence for a causal inverse model in an avian cortico-basal ganglia circuit

  • Recorded an stimulated the LMAN (upstream, modulatory) region of the zebrafinch song-production & learning pathway.
  • Found evidence, albeit weak, for a mirror arrangement or 'causal inverse' there: neurons fire bursts prior syllable production with some motor delay, ~30ms, and also fire single spikes with a delay ~10 ms to the same syllables.
    • This leads to an overall 'mirroring offset' of about 40 ms, which is sufficiently supported by the data.
    • The mirroring offset is quantified by looking at the cross-covariance of audio-synchronized motor and sensory firing rates.
  • Causal inverse: a sensory target input generates a motor activity pattern required to cause, or generate that same sensory target.
    • Similar to the idea of temporal inversion via memory.
  • Data is interesting, but not super strong; per the discussion, the authors were going for a much broader theory:
    • Normal Hebbian learning says that if a presynaptic neuron fires before a postsynaptic neuron, then the synapse is potentiated.
    • However, there is another side of the coin: if the presynaptic neuron fires after the postsynaptic neuron, the synapse can be similarly strengthened, permitting the learning of inverse models.
      • "This order allows sensory feedback arriving at motor neurons to be associated with past postsynaptic patterns of motor activity that could have caused this sensory feedback. " So: stimulate the sensory neuron (here hypothetically in LMAN) to get motor output; motor output is indexed in the sensory space.
      • In mammals, a similar rule has been found to describe synaptic connections from the cortex to the basal ganglia [37].
      • ... or, based on anatomy, a causal inverse could be connected to a dopaminergic VTA, thereby linking with reinforcement learning theories.
      • Simple reinforcement learning strategies can be enhanced with inverse models as a means to solve the structural credit assignment problem [49].
  • Need to review literature here, see how well these theories of cortical-> BG synapse match the data.