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Zawartość zarchiwizowana w dniu 2022-07-06

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OCCAM / socSMC Workshop on "Mechanisms and models of social interaction”

The goal of the OCCAM workshop series is to foster our understanding of mechanisms and principles of information processing in self-organised hierarchical and recurrent systems. Our knowledge of such systems is still very limited despite being a focus of research for many years. The OCCAM workshop series aims at assembling the latest results from various research branches in order to bring about a more comprehensive understanding of the principles of information processing.

6 Lipca 2017 - 8 Lipca 2017
Germany
The OCCAM (Osnabrück Computational Cognition Alliance Meeting) is organized by Peter König, Gordon Pipa and Frank Jäkel and brings together well established outstanding researchers as well as young promising scientists. This year, the 6th workshop of the series, jointly with the socSMCs project and coordinated with Andreas Engel (http://socsmcs.eu/(odnośnik otworzy się w nowym oknie)) we focus on social interactions. The topic comprises theoretical, computational, physiological and robotics approaches. The program includes full length talks, student presentations, a poster session, guided poster tours and a public talk to the general audience. We go to great lengths to actively involve all participants. Furthermore, there will also be time for informal discussions.

Osnabrück is a charming town with a long history many places worth visiting. It is conveniently located at the intersection of major train routes and has a local airport with good connections to major hubs (Frankfurt, Munich, London-City Airport) as well as a selection of further direct connections. More detailed information: www.occam-os.de

We'd be happy if this meeting is of interest to you or your students and hope to welcome you in Osnabrück in summer 2017.

Best,
Peter, Gordon, Frank & Andreas

Słowa kluczowe

Neural coding and representation of information in hierarchical systems, Self-organisation in dynamic systems, Mechanisms for probabilistic inference

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