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.
Model Type: Connectionist Network
Model Concept(s): Rate-coding model neurons; Reinforcement Learning; Unsupervised Learning; Attractor Neural Network; Winner-take-all; Hebbian plasticity; Olfaction
Simulation Environment: C or C++ program
Implementer(s): Weber, Cornelius [cweber at fias.uni-frankfurt.de]; Elshaw, Mark [mark.elshaw at sunderland.ac.uk]
Weber C, Wermter S, Elshaw M. (2006). A hybrid generative and predictive model of the motor cortex. Neural networks : the official journal of the International Neural Network Society 19 [PubMed]
Triesch J, Weber C. (2006). A possible representation of reward in the learning of saccades Proc. of the Sixth International Workshop on Epigenetic Robots
Weber C, Wermter S. (2006/7). A self-organizing map of sigma-pi units Neurocomputing 70(13-15)