Abstract
The objective of this work is to use a multi-core embedded platform as computing architectures for neural applications relevant to neuromorphic engineering: e.g., robotics, and artificial and spiking neural networks. Recently, it has been shown how spike-timing-dependent plasticity (STDP) can play a key role in pattern recognition. In particular, multiple repeating arbitrary spatio-temporal spike patterns hidden in spike trains can be robustly detected and learned by multiple neurons equipped with spike-timing-dependent plasticity listening to the incoming spike trains. This paper presents an implementation on a biological time scale of STDP algorithm to localize a repeating spatio-temporal spike patterns on a multi-core embedded platform.
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Acknowledgements
This work was financially supported by the “PHC Sakura” program (project number: 35966TL), implemented by the French Ministry of Foreign Affairs, the French Ministry of Higher Education and Research, and the Japan Society for Promotion of Science. This work was also supported by NEC Corporation.
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This work was presented in part at the 22nd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 19–21, 2017.
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Grassia, F., Levi, T., Doukkali, E. et al. Spike pattern recognition using artificial neuron and spike-timing-dependent plasticity implemented on a multi-core embedded platform. Artif Life Robotics 23, 200–204 (2018). https://doi.org/10.1007/s10015-017-0421-y
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DOI: https://doi.org/10.1007/s10015-017-0421-y