Spiking neural networks for inference and learning: a memristor-based design perspective

Mohammed E. Fouda, Fadi Kurdahi, Ahmed Eltawil, Emre Neftci

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Scopus citations


On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet been success, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore such plasticity rules generally show much higher performance than that of classical STDP rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware.

Original languageEnglish (US)
Title of host publicationMemristive Devices for Brain-Inspired Computing
Subtitle of host publicationFrom Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks
Number of pages32
ISBN (Electronic)9780081027820
StatePublished - Jan 1 2020


  • Brain-inspired computing
  • Deep learning
  • In-memory computing
  • Memristors
  • Nonindealities
  • Three-factor rules

ASJC Scopus subject areas

  • General Engineering


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