title: A hybrid generative and predictive model of the motor cortex.
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.
authors.affiliation: Centre for Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland SR6 0DD, UK. cornelius.weber@sunderland.ac.uk
authors.forename: Cornelius
authors.initials: C
authors.lastname: Weber
authors.forename: Stefan
authors.initials: S
authors.lastname: Wermter
authors.forename: Mark
authors.initials: M
authors.lastname: Elshaw
mesh_terms: D001931:Brain Mapping; D006801:Humans; D008954:Models, Biological; D008960:Models, Psychological; D009044:Motor Cortex / Q000033:anatomy & histology / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology; D011237:Predictive Value of Tests; D011597:Psychomotor Performance; D012054:Reinforcement, Psychology