Neuromorphic systems emulate the organization and function of nervous systems. They are
usually composed of analogue electronic circuits that are fabricated in the complementary …

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 …

The authors guide readers quickly and concisely through the complex topics of neural
networks, fuzzy logic, mathematical modelling of electrical machines, power systems control …

This paper presents a brief review of some analog hardware implementations of neural
networks. Several criteria for the classification of general neural networks implementations …

A neuromorphic circuit includes a first field effect transistor in a first diode configuration
establishing an electrical connection between a first gate and a first drain of the first field …

Adaptive interaction is a new approach to introduce adaptability into man-made systems. In
this approach, a system is decomposed into interconnected subsystems that we call devices …

This paper describes elements necessary for a general-purpose low-cost very large scale
integration (VLSI) neural network. By choosing a learning algorithm that is tolerant of analog …

Owing to the data explosion and rapid development of artificial intelligence (AI), particularly
deep neural networks (DNNs), the ever-increasing demand for large-scale matrix-vector …

Analog VLSI implementations of artificial neural networks are usually considered efficient for
the small area and the low power consumption they require, but very poor in terms of …

A circuit technology for self-learning neural network hardware has been developed using a
high-functionality device called Neuron MOS Transistor (/spl upsi/MOS) as a key circuit …