Learning memristive spiking neurons and beyond

Olga Krestinskaya, Alex James

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


The design and on-chip implementation of learning algorithms for neuromorphic spike domain memristive architectures is a challenging problem. In this chapter, we provide a short overview of the challenges, open problems, architectures and state of the art implementations of spike-based CMOS-memristive neural networks and systems. The importance of biomimicry, the feasibility of scalability, large-scale information processing, data rate challenges, and building a system of systems make this a vibrant topic for discussion.

Original languageEnglish (US)
Title of host publicationMem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Number of pages12
ISBN (Electronic)9780128211847
StatePublished - Jan 1 2021


  • HTM
  • Learning
  • LSTM
  • Spiking neural network

ASJC Scopus subject areas

  • Engineering(all)
  • Arts and Humanities(all)


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