Over the past few years, extensive work on optical neural networks has been investigated in
hopes of achieving orders of magnitude improvement in energy efficiency and compute …

Write-variability and resistance instability are major reliability concerns impeding
implementation of oxide-based memristive devices in neuromorphic systems. The root …

Memristive devices such as resistive switching memories and artificial synapses have
emerged as promising technologies to overcome the technological challenges associated …

Organic artificial neurons operating in liquid environments are crucial components in
neuromorphic bioelectronics. However, the current understanding of these neurons is …

The paper presents an analysis of current state and perspectives of high-performance
computing based on the principles of information storage and processing in biological …

In today's data-centric world, where data fuels numerous application domains, with machine
learning at the forefront, handling the enormous volume of data efficiently in terms of time …

In this study, we investigated the resistive switching (RS) characteristics of ZrO x/HfO x
bilayer-based resistive random-access memory (RRAM) devices. A 1.5-nm-thick HfO x layer …

In-memory computing (IMC) using emerging nonvolatile devices has received considerable
attention due to its great potential for accelerating artificial neural networks and machine …

A neuromorphic system is composed of hardware-based artificial neurons and synaptic
devices, designed to improve the efficiency of neural computations inspired by energy …

Brain–computer interfaces (BCIs) that enable human–machine interaction have immense
potential in restoring or augmenting human capabilities. Traditional BCIs are realized based …