Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Synthetic brain imaging: grasping, mirror neurons and imitation
Neural Networks - Special issue on the global brain: imaging and modelling
Self-Organizing Maps
A haptic system for the lucs haptic hand I
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
Editorial: Mobile robotics in the UK and worldwide: Fast changing, and as exciting as ever
Robotics and Autonomous Systems
Ikaros: Building cognitive models for robots
Advanced Engineering Informatics
Active categorical perception of object shapes in a simulated anthropomorphic robotic arm
IEEE Transactions on Evolutionary Computation
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Three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. One model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (SOM). The two other models replace the tensor product operation with a novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). The two T-MPSOM models differ in the procedure employed to calculate the neural activation. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. All the models learned to map different shapes to different areas of the SOM, and the tensor product model as well as one of the T-MPSOM models also learned to discriminate individual test objects.