TY - GEN
T1 - Retina color-opponency based pursuit implemented through spiking neural networks in the neurorobotics platform
AU - Ambrosano, Alessandro
AU - Vannucci, Lorenzo
AU - Albanese, Ugo
AU - Kirtay, Murat
AU - Falotico, Egidio
AU - Martínez-Cañada, Pablo
AU - Hinkel, Georg
AU - Kaiser, Jacques
AU - Ulbrich, Stefan
AU - Levi, Paul
AU - Morillas, Christian
AU - Knoll, Alois
AU - Gewaltig, Marc Oliver
AU - Laschi, Cecilia
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84978877412&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42417-0_2
DO - 10.1007/978-3-319-42417-0_2
M3 - Conference contribution
AN - SCOPUS:84978877412
SN - 9783319424163
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 16
EP - 27
BT - Biomimetic and Biohybrid Systems - 5th International Conference, Living Machines 2016, Proceedings
A2 - Lepora, Nathan F.
A2 - Mura, Anna
A2 - Desmulliez, Marc
A2 - Mangan, Michael
A2 - Verschure, Paul F.M.J.
A2 - Prescott, Tony J.
PB - Springer Verlag
T2 - 5th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2016
Y2 - 19 July 2016 through 22 July 2016
ER -