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Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles.
Marc-Oliver Gewaltig, Luc Guyot, Egidio Falotico, Lorenzo Vannucci, Axel von Arnim, Paul Levi, Georg Hinkel, Stefan Ulbrich, Eduardo Ros, Alessandro Ambrosano, Nino Cauli, Patrick Van Der Smagt, Oliver Denninger, Ugo Albanese, Murat Kirtay, Daniel Peppicelli, Stefan Deser, Sandro Weber, Patrick Maier, Igor Peric
Francesco Mondada, Frank Bonnet