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Corollary discharge, internal signals predicting sensory consequences of actions, attenuates self-stimulation. A new model explains this neural mechanism using a three-factor learning rule for prediction error computation.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Corollary discharge predicts sensory outcomes of self-generated actions.
  • This prediction mechanism attenuates responses to self-produced stimuli.
  • Understanding the neural basis of this predictive processing is crucial.

Purpose of the Study:

  • To investigate the neural circuit mechanisms underlying corollary discharge.
  • To develop a biologically plausible computational model for prediction error computation.

Main Methods:

  • Introduction of a novel three-factor learning rule.
  • Development of a neural network model with prediction error neurons.
  • Analysis of neural data from motor-visual and motor-auditory mismatch experiments in mice.

Main Results:

  • The model successfully accounts for observed motor-visual and motor-auditory mismatch responses.
  • The model predicts a bimodal distribution of activity correlation with prediction, supported by experimental data.
  • The study links global modulatory signals to local synaptic plasticity for predictive error calculation.

Conclusions:

  • The proposed three-factor learning rule provides a viable mechanism for corollary discharge.
  • Disruptions in inhibitory processes can impair mismatch computation in specific ways.
  • The findings offer insights into predictive coding in sensory systems.