A simulator and the configuration files for three publications are
provided. First, "A hybrid generative and predictive model of the motor
cortex" (Weber at al. 2006) which uses reinforcement learning to set up a
toy action scheme, then uses unsupervised learning to "copy" the learnt
action, and an attractor network to predict the hidden code of the
unsupervised network. Second, "A Self-Organizing Map of Sigma-Pi Units"
(Weber and Wermter 2006/7) learns frame of reference transformations on
population codes in an unsupervised manner. Third, "A possible
representation of reward in the learning of saccades" (Weber and Triesch,
2006) implements saccade learning with two possible learning schemes for
horizontal and vertical saccades, respectively.
Weber C, Wermter S, Elshaw M (2006) A hybrid generative and predictive model of the motor cortex. Neural Netw 19:339-53 [PubMed]
Weber C, Triesch J (2006) A possible representation of reward in the learning of saccades Proc. of the Sixth International Workshop on Epigenetic Robots :153-60
Weber C, Wermter S (2006) A self-organizing map of sigma-pi units Neurocomputing 70(13-15):2552-2560