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The 'red-green' pathway of the retina is classically recognized as one of the retinal mechanisms allowing humans to gather color information from light, by combining information from L-cones and M-cones in an opponent way. The precise retinal circuitry that allows the opponency process to occur is still uncertain, but it is known that signals from L-cones and M-cones, having a widely overlapping spectral response, contribute with opposite signs. In this paper, we simulate the red-green opponency process using a retina model based on linear-nonlinear analysis to characterize context adaptation and exploiting an image-processing approach to simulate the neural responses in order to track a moving target. Moreover, we integrate this model within a visual pursuit controller implemented as a spiking neural network to guide eye movements in a humanoid robot. Tests conducted in the Neurorobotics Platform confirm the effectiveness of the whole model. This work is the first step towards a bio-inspired smooth pursuit model embedding a retina model using spiking neural networks.
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