Learning memristive spiking neurons and beyond

Abstract 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.