Multimodal communication in animals, humans and robots: an introduction to perspectives in brain-inspired informatics.

Recent years have seen convergence in research on brain mechanisms and neurocomputational approaches, culminating in the creation of a new generation of robots whose artificial "brains" respect neuroscience principles and whose "cognitive" systems venture into higher cognitive domains such as planning and action sequencing, complex object and concept processing, and language. The present article gives an overview of selected projects in this general multidisciplinary field. The work reviewed centres on research funded by the EU in the context of the New and Emergent Science and Technology, NEST, funding scheme highlighting the topic "What it means to be human". Examples of such projects include learning by imitation (Edici project), examining the origin of human rule-based reasoning (Far), studying the neural origins of language (Neurocom), exploring the evolutionary origins of the human mind (Pkb140404), researching into verbal and non-verbal communication (Refcom), using and interpreting signs (Sedsu), characterising human language by structural complexity (Chlasc), and representing abstract concepts (Abstract). Each of the communication-centred research projects revealed individual insights; however, there had been little overall analysis of results and hypotheses. In the Specific Support Action Nestcom, we proposed to analyse some NEST projects focusing on the central question "What it means to communicate" and to review, understand and integrate the results of previous communication-related research, in order to develop and communicate multimodal experimental hypotheses for investigation by future projects. The present special issue includes a range of papers on the interplay between neuroinformatics, brain science and robotics in the general area of higher cognitive functions and multimodal communication. These papers extend talks given at the NESTCOM workshops, at ICANN (http://www.his.sunderland.ac.uk/nestcom/workshop/icann.html) in Porto and at the first meeting of the Federation of the European Societies of Neuropsychology in Edinburgh in 2008 (http://www.his.sunderland.ac.uk/nestcom/workshop/esn.html). We hope that the collection will give a vivid insight into current trends in the field.

Neural fuzzy preference integration using neural preference Moore machines.

This paper describes preference classes and preference Moore machines as a basis for integrating different hybrid neural representations. Preference classes are shown to provide a basic link between neural preferences and fuzzy representations at the preference class level. Preference Moore machines provide a link between recurrent neural networks and symbolic transducers at the preference Moore machine level. We demonstrate how the concepts of preference classes and preference Moore machines can be used to interpret neural network representations and to integrate knowledge from hybrid neural representations. One main contribution of this paper is the introduction and analysis of neural preference Moore machines and their link to a fuzzy interpretation. Furthermore, we illustrate the interpretation and combination of various neural preference Moore machines with additional real-world examples.