Mod-DeepESN: Modular Deep Echo State Network

@article{Carmichael2018ModDeepESNMD,
  title={Mod-DeepESN: Modular Deep Echo State Network},
  author={Zachariah Carmichael and Humza Syed and Stuart Burtner and Dhireesha Kudithipudi},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.00523}
}
Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices. The baseline echo state network algorithms are shown to be efficient in solving small-scale spatio-temporal problems. However, they underperform for complex tasks that are characterized by multi-scale structures. In this research, an intrinsic plasticity-infused modular deep echo state network architecture is proposed to solve complex and… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 16 REFERENCES

Robustness of a memristor based liquid state machine

  • 2017 International Joint Conference on Neural Networks (IJCNN)
  • 2017
VIEW 1 EXCERPT

jul). DEAP: Evolutionary algorithms made easy

Fortin, F.-A, +5 authors C. Gagné
  • Journal of Machine Learning Research,
  • 2012
VIEW 2 EXCERPTS