Abstract
In this paper, we present the numerical results of the implementation of a Spiking Central Pattern Generator (SCPG) on a SpiNNaker board. The SCPG is a network of current-based leaky integrate-and-fire (LIF) neurons, which generates periodic spike trains that correspond to different locomotion gaits (i.e. walk, trot, run). To generate such patterns, the SCPG has been configured with different topologies, and its parameters have been experimentally estimated. To validate our designs, we have implemented them on the SpiNNaker board using PyNN and we have embedded it on a hexapod robot. The system includes a Dynamic Vision Sensor system able to command a pattern to the robot depending on the frequency of the events fired. The more activity the DVS produces, the faster that the pattern that is commanded will be.
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Acknowledgements
This work is partially supported by the Spanish government grant (with support from the European Regional Development Fund) COFNET (TEC2016-77785-P). Also, this work has been supported by the Mexican government through the CONACYT project “Aplicación de la Neurociencia Computacional en el Desarrollo de Sistemas Roboticos Biologicamente Inspirados” (269798). The work of Juan P. Dominguez-Morales was supported by a Formación de Personal Universitario Scholarship from the Spanish Ministry of Education, Culture and Sport.
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Cuevas-Arteaga, B. et al. (2017). A SpiNNaker Application: Design, Implementation and Validation of SCPGs. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_47
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DOI: https://doi.org/10.1007/978-3-319-59153-7_47
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