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This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled)…

Single-molecule magnets weakly coupled to two ferromagnetic leads act as memory devices in electronic circuits---their response depends on history, not just on the instantaneous applied voltage. We show that magnetic anisotropy introduces a…

Developing electronic devices capable of emulating biological functions is essential for advancing brain-inspired computation paradigms such as neuromorphic computing. In recent years, two-dimensional materials have emerged as promising…

Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when…

Magnetic skyrmions, as scalable and non-volatile spin textures, can dynamically interact with fields and currents, making them promising for unconventional computing. This paper presents a neuromorphic device based on skyrmion manipulation…

In this work, an optimized method was implemented for attaining stable multibit operation with low energy consumption in a two-terminal memory element made from the following layers: Ag/Pt nanoparticles (NPs)/SiO2/TiN in a…

Spin-torque memristors were proposed in 2009, which could provide fast, low-power and infinite memristive behavior for large-density non-volatile memory and neuromorphic computing. However, the strict requirements of combining high…

Computing inspired by the human brain requires a massive parallel architecture of low-power consuming elements of which the internal state can be changed. SrTiO3 is a complex oxide that offers rich electronic properties; here Schottky…

Memristive devices have been considered promising candidates for nature-inspired computing and in-memory information processing. However, experimental devices developed to date typically show significant variability and function at…

Emergent learning transforms a disordered optical medium into a photonic device capable of storage, recognition, and classification of arbitrary memory patterns. First, we show that the intensity at the output of a multiply scattering…

Mimicking the collective excitatory and inhibitory behaviors of biological neurons remains a critical challenge in the development of neuromorphic computing systems that rival the complexity and performance of the human brain. Volatile…

Optical materials with special optical properties are widely used in a broad span of technologies, from computer displays to solar energy utilization leading to large dataset accumulated from years of extensive materials synthesis and…

The growing interest in bio-inspired materials is driven by the need for increasingly targeted and efficient devices that also have a low ecological impact. These devices often use specially developed materials (e.g., polymers, aptamers,…

Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal performance demonstrated at dynamical phase transitions. In…

The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain. Based on multi-disciplinary…

We experimentally demonstrate a proof-of-principle implementation of an almost ideal memristor - a two-terminal circuit element whose resistance is approximately proportional to the integral of the input signal over time. The demonstrated…

Ion transport through nanopores permeates through many areas of science and technology, from cell behavior to sensing and separation to catalysis and batteries. Two-dimensional materials, such as graphene, molybdenum disulfide (MoS$_2$),…

Brain-inspired learning mechanisms, e.g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network. When incorporating emerging nanoscale resistive non-volatile memory (NVM)…

Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating…

The rapid expansion in the spectrum of two-dimensional (2D) materials has driven the efforts of research on the fabrication of 2D composites and heterostructures. Highly ordered structure of 2D materials provides an excellent platform for…