A hybrid generative and predictive model of the motor cortex

Authors:
Cornelius Weber;Stefan Wermter;Mark Elshaw
Affiliations:
Centre for Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland SR6 0DD, UK;Centre for Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland SR6 0DD, UK;Centre for Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland SR6 0DD, UK
Venue:
Neural Networks
Year:
2006

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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 attractor network 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.