A hybrid generative and predictive model of the motor cortex

Cornelius Weber, Stefan Wermter, Mark Elshaw

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

We describe a hybrid generative and predictive model of the motor cortex. The generative model is related to the hierarchically directed cortico-cortical (or thalamo-cortical) connections and unsupervised training leads to a topographic and sparse hidden representation of its sensory and motor input. The predictive model is related to lateral intra-area and inter-area cortical connections, functions as a hetero-associator and is trained to predict the future state of the network. Applying partial input, the generative model can map sensory input to motor actions and can thereby perform learnt action sequences of the agent within the environment. The predictive model can additionally predict a longer perception- and action sequence (mental simulation). The models’ performance is demonstrated on a visually guided robot docking manoeuvre. We propose that the motor cortex might take over functions previously learnt by reinforcement in the basal ganglia and relate this to mirror neurons and imitation
Original languageEnglish
Pages (from-to)339-353
Number of pages15
JournalNeural Networks
Volume19
Issue number4
DOIs
Publication statusPublished - May 2006
Externally publishedYes

Keywords

  • Motor cortex
  • Basal ganglia
  • Forward model
  • Unsupervised learning
  • Supervised learning
  • Reinforcement learning
  • Helmholtz machine
  • Continuous attractor network

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