Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

  title={Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines},
  author={Emre Neftci and Charles Augustine and Somnath Paul and Georgios Detorakis},
  booktitle={Front. Neurosci.},
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of… CONTINUE READING
Highly Cited
This paper has 26 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 41 times over the past 90 days. VIEW TWEETS


Publications citing this paper.
Showing 1-10 of 19 extracted citations


Publications referenced by this paper.
Showing 1-10 of 85 references

Target propagation

  • Lee, D.-H., S. Zhang, A. Biard, Y. Bengio
  • arXiv preprint arXiv:1412.7525.
  • 2014
Highly Influential
4 Excerpts

Spike timing-dependent plasticity in the Frontiers in Neuroscience

  • R. J. Vogelstein, F. Tenore, R. Philipp, M. S. Adlerstein, D. H. Goldberg, G. Cauwenberghs
  • 2002
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…