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Electrical synapses (gap junctions)
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Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)
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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.
Weber C, Triesch J (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) A self-organizing map of sigma-pi units
(Click on a link to find other models with that property)
C or C++ program;
Rate-coding model neurons;
Attractor Neural Network;
Weber, Cornelius [cweber at fias.uni-frankfurt.de];
Elshaw, Mark [mark.elshaw at sunderland.ac.uk];
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