Spike pattern recognition using artificial neuron and Spike-Timing-Dependent Plasticity implemented on a multi-core embedded platform

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, 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 spatiotemporal 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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Filippo Grassia, Timothée Levi, E Doukkali, T Kohno. Spike pattern recognition using artificial neuron and Spike-Timing-Dependent Plasticity implemented on a multi-core embedded platform. 22th International Symposium on Artificial Life and Robotics, Jan 2017, Beppu, Japan. ⟨hal-01567495⟩

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