Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

Ion migration as well as electron transfer and coupling in resistive switching materials
endow memristors with a physically tunable conductance to resemble synapses, neurons …

In the quest for emerging in-sensor computing, materials that respond to optical stimuli in
conjunction with non-volatile phase transition are highly desired for realizing bioinspired …

Neuromorphic computing based on spikes offers great potential in highly efficient computing
paradigms. Recently, several hardware implementations of spiking neural networks based …

Complex-oxide materials exhibit a vast range of functional properties desirable for next-
generation electronic, spintronic, magnetoelectric, neuromorphic, and energy conversion …

Many correlated systems feature an insulator-to-metal transition that can be triggered by an
electric field. Although it is known that metallization takes place through filament formation …

Due to the increasing importance of artificial intelligence (AI), significant recent effort has
been devoted to the development of neuromorphic circuits that seek to emulate the energy …

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-
memory computing with synaptic devices. However, compact nanodevices implementing …

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 …

Neuromorphic computing has shown remarkable capabilities in silicon-based artificial
intelligence, which can be optimized by using Mott materials for functional synaptic …