Neuromorphic Neural Network Parallelization on CUDA Compatible GPU for EEG Signal Classification

  title={Neuromorphic Neural Network Parallelization on CUDA Compatible GPU for EEG Signal Classification},
  author={L{\'a}szl{\'o} Bak{\'o} and Arpad-Zoltan Kolcsar and S{\'a}ndor-Tiham{\'e}r Brassai and Laszlo-Ferenc Marton and Lajos Losonczi},
  journal={2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation},
The purpose of the project described in this paper is to implement a Spiking Neural Network, on a CUDA driven Nvidia video-card, which can learn predefined samples on images presented as input data. With experimental EEG signals pre-processed using the Wavelet transform into an image set, it can learn to classify inputs into a certain category by applying a proprietary algorithm, presented in the paper. The implementation of the spiking neural network is done in CUDA C, with the use of the card… CONTINUE READING


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View 1 Excerpt

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View 1 Excerpt

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