This talk will be subdivided into two parts.
After an introduction of the original Recurrent Neural Network with Parametric Bias (RNNPB) architecture an improved version (iRNNPB), with respect to stability, speed of convergence and accuracy is presented. The applied changes that lead to this boost in performance are elucidated and examples are given showing the intriguing capabilities of this recurrent neural network, including storage and retrieval, recognition and generalization of time series.
In the second part the iRNNPB is applied in a real world robot scenario for action-driven classification of object categories. It will be shown how the architecture handles real world multi-modal sensorimotor data acquired from a humanoid robot and how it compares to canonical methods used for classification. Furthermore, the generalization potential concerning object attributes, its robustness against noise and how this can be used for de-noising of sensory channels is demonstrated.