SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure

@article{Wang2017SpikeTempAE,
  title={SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure},
  author={Jinling Wang and Ammar Belatreche and Liam P. Maguire and T. Martin McGinnity},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2017},
  volume={28},
  pages={30-43}
}
This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population… CONTINUE READING
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