Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes

@article{Moraitis2017FatiguingSL,
  title={Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes},
  author={Timoleon Moraitis and Abu Sebastian and Irem Boybat and Manuel Le Gallo and Tomas Tuma and Evangelos Eleftheriou},
  journal={2017 International Joint Conference on Neural Networks (IJCNN)},
  year={2017},
  pages={1823-1830}
}
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates. In this article, we propose a spike-timing… 
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