The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.

The recent achievements in luminescent nanomaterials used in optical and optoelectronic data storage have been reviewed.

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By virtue of energy efficiency, high speed, and parallelism, brain‐inspired neuromorphic computing is a promising technology to overcome the von Neumann bottleneck and capable of processing massive sophisticated tasks in the background of big data. The abilities of perceiving and reacting to events in artificial neuromorphic systems allow us to build the communicative electronic–biological interfaces to get closer to electronic life. Protein materials offer great application potentials in such a system due to their sustainability, low cost, controllable hierarchical structure, intrinsic biocompatibility, and biodegradability. Herein, a timely review of the development of protein‐based memories for data storage and neuromorphic computing is provided. Proteins’ unique mechanical, electronic, optical properties, and their broad applications are discussed. Then, the progress of protein‐based two‐terminal memristor and three‐terminal transistor‐type memory is reviewed, and their applications for data storage, logic circuit, and neuromorphic computing are introduced. Finally, the major challenges and outlook toward the future developing directions of protein‐based computing systems are pointed out.

Resistive random access memory (RRAM) based on hybrid organic-inorganic halide perovskite (HOIP) has recently gained significant interests due to its low activation energy of ion migration. HOIP RRAM has been...

Emerging intelligent devices that can simulate an artificial intelligence vision system are of great interest for the development of modern information technology. Nociceptor is a crucial sensory neuron that recognizes harmful inputs and sends pain signals to the central nervous system to avoid injury; however, visual nociceptors, considered to be a key bionic function to protect eyesight based on optoelectronic devices, have yet to be developed. Herein this study, a three‐terminal flexible memory phototransistor (MPT) is first fabricated, which simulates the visual nociceptive behavior by adjusting light stimulation. The CsPbBr3 quantum‐dots (QDs)‐few‐layered black phosphorous nanosheets (FLBP NSs) heterojunction MPT demonstrates high responsivity of 7.2 × 103 AW−1 and high detectivity of 1.8 × 1013 Jones due to the high absorption coefficient of CsPbBr3 QDs materials and a high carrier transport property of FLBP NSs. Moreover, the proposed device can be used to emulate ultraviolet‐stimuli‐induced characteristics of visual nociceptors such as a threshold, no adaption, relaxation, allodynia, and hyperalgesia. It provides a new avenue for the realization of next‐generation neural‐integrated devices via its visual pain sense‐perception abilities.

The saturation of Moore’s law and the finality of Dennard scaling mark the requirements for new data-storage approaches employing different physical mechanisms. Due to the low operation voltage, multibit storage...

Memristive devices and systems have emerged as powerful technologies to fuel neuromorphic chips. However, the traditional two-terminal memristor still suffers from nonideal device characteristics, raising challenges for its further application in versatile biomimetic emulation for neuromorphic computing owing to insufficient control of filament forming for filamentary-type cells and a transport barrier for interfacial switching cells. Here, we propose three-terminal memristors with a top-gate field-effect geometry by employing a ferroelectric material, poly(vinylidene fluoride–trifluoroethylene), as the dielectric layer. This approach can finely modulate ion transport and contact barrier at the switching interface in non-filamentary perovskite memristors, thus, creating two distinct operation modes (volatile and nonvolatile). Additionally, perovskite memristors show desirable resistive switching performance, including forming-free operation, high yield of 88.9%, cycle-to-cycle variation of 7.8%, and low operating current of sub-100 nA. The dual-mode memristor is capable of emulating biological nociception in both active (perceiving pain) and blocked states (suppressing pain signaling).