Yifei Yang

TL;DR: Experimental results and nanoparticle dynamic simulations both showed that Ag atoms from double sources could mimic Ca2+ dynamics in the pre- and post-synaptic terminals under stimuli, indicating that the self-doping memristors are promising components for future hardware creation of neuromorphic systems and emulate the characteristics of the brain.

TL;DR: Yang et al. as discussed by the authors proposed a Te-based resistive switching cross-point array for high-density storage, in-memory computing and neuromorphic computing, which is able to function as either selector or memory in respective desired current ranges.

TL;DR: The development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions.

TL;DR: This work broadens the neuromorphic applications of Te resistive switching devices from synaptic array devices to artificial neurons, which can exhibit self‐sustained oscillatory behavior when stimulated by a steady DC voltage as a result of the repetitive electrochemical growth and the ensuing Joule heat‐induced thermal melting of the Te filament.

TL;DR: In this article, the authors demonstrate tellurium-based resistive switching (RS) cross-point arrays for high-density data storage, in-memory computing and neuromorphic computing.