Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Neuromorphic Engineering has emerged as an exciting research area, primarily owing to
the paradigm shift from conventional computing architectures to data-driven, cognitive …

The standard nature of computing is currently being challenged by a range of problems that
start to hinder technological progress. One of the strategies being proposed to address …

Neuromorphic computing covers a diverse range of approaches to information processing
all of which demonstrate some degree of neurobiological inspiration that differentiates them …

Neuromorphic computing was originally referred to as the hardware that mimics neuro-
biological architectures to implement models of neural systems. The concept was then …

Neuromorphic engineering combines the architectural and computational principles of
systems neuroscience with semiconductor electronics, with the aim of building efficient and …

Neuromorphic computing is becoming a popular approach for implementations of brain-
inspired machine learning tasks. As a paradigm for both hardware and algorithm design …

The brain is not a glorified digital computer. It does not store information in registers, and it
does not mathematically transform mental representations to establish perception or …