Synaptic devices, including synaptic memristor and synaptic transistor, are emerging
nanoelectronic devices, which are expected to subvert traditional data storage and …

Neuromorphic computing could address the inherent limitations of conventional silicon
technology in dedicated machine learning applications. Recent work on silicon-based …

Facing the exponential growth of data digital communications and the advent of artificial
intelligence, there is an urgent need for information technologies with huge storage capacity …

Functional emulation of biological synapses using electronic devices is regarded as the first
step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte …

Neuromorphic systems consisting of artificial neurons and memristive synapses could
provide a much better performance and a significantly more energy-efficient approach to the …

An array of cross-point memristive devices has been implemented on the basis of yttria-
stabilized zirconia thin film for applications in prototypes of spiking neural networks. The …

Polyaniline (PANI) based memristive devices have emerged as promising candidates for
hardware implementation of artificial synapses (the key components of neuromorphic …

Neuromorphic systems must have at least five unavoidable features that are present in living
beings. First, neuromorphic systems must perform memorizing and processing functions …

The development of memristor-based spiking neuromorphic systems (NS) has been
essentially driven by the hope to replicate the extremely high energy efficiency of biological …

Spiking neural networks (SNNs) are believed to be highly computationally and energy
efficient for specific neurochip hardware real-time solutions. However, there is a lack of …