Retina Color-Opponency Based Pursuit Implemented Through Spiking Neural Networks in the Neurorobotics Platform

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.

Lepora, Nf
Mura, A
Mangan, M
Verschure, Pfmj
Desmulliez, M
Prescott, Tj
Published in:
Biomimetic And Biohybrid Systems, Living Machines 2016, 9793, 16-27
Presented at:
5th International Conference on Biomimetic and Biohybrid Systems (Living Machines), Edinburgh, SCOTLAND, JUL 19-22, 2016
Cham, Springer Int Publishing Ag
978-3-319-42417-0; 978-3-319-42416-3

 Record created 2017-01-24, last modified 2018-03-17

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