A hybrid generative and predictive model of the motor cortex
Neural Networks
Volume 9,
Number 4,
pages 339--353,
doi: 10.1016/j.neunet.2005.10.004
- Jun 2006
Associated documents :
We describe a hybrid generative and predictive model of the motor cortex. The generative model is related to the hierarchically directed corticocortical (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.
@Article{WWE06,
author = {Weber, Cornelius and Wermter, Stefan and Elshaw, Mark I.},
title = {A hybrid generative and predictive model of the motor cortex},
journal = {Neural Networks},
number = {4},
volume = {9},
pages = {339--353},
year = {2006},
month = {Jun},
publisher = {Elsevier},
doi = {10.1016/j.neunet.2005.10.004},
}