  Neuromorphic engineering (NE) encompasses a diverse range of approaches to
information processing that are inspired by neurobiological systems, and this
feature distinguishes neuromorphic systems from conventional computing systems.
The brain has evolved over billions of years to solve difficult engineering
problems by using efficient, parallel, low-power computation. The goal of NE is
to design systems capable of brain-like computation. Numerous large-scale
neuromorphic projects have emerged recently. This interdisciplinary field was
listed among the top 10 technology breakthroughs of 2014 by the MIT Technology
Review and among the top 10 emerging technologies of 2015 by the World Economic
Forum. NE has two-way goals: one, a scientific goal to understand the
computational properties of biological neural systems by using models
implemented in integrated circuits (ICs); second, an engineering goal to
exploit the known properties of biological systems to design and implement
efficient devices for engineering applications. Building hardware neural
emulators can be extremely useful for simulating large-scale neural models to
explain how intelligent behavior arises in the brain. The principle advantages
of neuromorphic emulators are that they are highly energy efficient, parallel
and distributed, and require a small silicon area. Thus, compared to
conventional CPUs, these neuromorphic emulators are beneficial in many
engineering applications such as for the porting of deep learning algorithms
for various recognitions tasks. In this review article, we describe some of the
most significant neuromorphic spiking emulators, compare the different
architectures and approaches used by them, illustrate their advantages and
drawbacks, and highlight the capabilities that each can deliver to neural
modelers.
