Towards biomimetic neural learning for intelligent robots

Authors:
Stefan Wermter;Günther Palm;Cornelius Weber;Mark Elshaw
Affiliations:
Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland, UK;Neuroinformatics, University of Ulm, Ulm, Germany;Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland, UK;Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland, Sunderland, UK
Venue:
Biomimetic Neural Learning for Intelligent Robots
Year:
2005

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Abstract

We present a brief overview of the chapters in this book that relate to the development of intelligent robotic systems that are inspired by neuroscience concepts. Firstly, we concentrate on the research of the MirrorBot project which focuses on biomimetic multimodal learning in a mirror neuron-based robot. This project has made significant developments in biologically inspired neural models using inspiration from the mirror neuron system and modular cerebral cortex organisation of actions for use in an intelligent robot within an extended ‘pick and place' type scenario. The hypothesis under investigation in the MirrorBot project is whether a mirror neuron-based cell assembly model can produce a life-like perception system for actions. Various models were developed based on principles such as cell assemblies, associative neural networks, and Hebbian-type learning in order to associate vision, language and motor concepts. Furthermore, we introduce the chapters of this book from other researchers who attended our AI-workshop on NeuroBotics.