Spike pattern recognition using artificial neuron and spike-timing-dependent plasticity implemented on a multi-core embedded platform |
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Authors: | F Grassia T Levi E Doukkali T Kohno |
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Affiliation: | 1.LTI Lab,University of Picardie Jules Verne,Amiens,France;2.IMS Lab,University of Bordeaux,Bordeaux,France;3.Institute of Industrial Science (IIS),the University of Tokyo,Tokyo,Japan |
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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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