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Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120469 | PMC |
http://dx.doi.org/10.1080/14686996.2023.2188878 | DOI Listing |
Micromachines (Basel)
July 2025
Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA.
Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements.
View Article and Find Full Text PDFMicromachines (Basel)
July 2025
Institute of Advanced Technology, Vietnam Academy of Science and Technology, 1 Mac Dinh Chi, Ho Chi Minh City 70072, Vietnam.
In this work, research on liquid-based resistive switching devices is carried out, using bottom printable electrodes fabricated from Silver (Ag) paste and silver nitrate (AgNO) solution. The self-crossing I-V curves are observed and repeatedly shown by applying 100 sweep cycles, demonstrating repeatability and stability. This liquid device can be refreshed by adding extra droplets of AgNO so that self-crossing I-V hysteresis with up to 493 dual sweeps can be obtained.
View Article and Find Full Text PDFMicromachines (Basel)
July 2025
Systems Integration & Emerging Energies (SI2E), Electrical Engineering Department, National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia.
This study presents transistor-level simulation results for a novel memristor emulator circuit. The design incorporates an inverter and a current-mode-controlled operational transconductance amplifier to stabilize the output voltage. Transient performance is evaluated across a 20 MHz to 100 MHz frequency range.
View Article and Find Full Text PDFSci Rep
August 2025
Institute of Electromagnetic Fields (IEF), ETH Zurich, Zurich, 8092, Switzerland.
Unlabelled: Memristive devices have drawn significant interest due to their use in novel paradigms such as neuromorphic computing. Neuromorphic systems are developed by implementing artificial neurons and synapses on a hardware level. Hence, memristors with multipurpose and reconfigurable neuromorphic functionalities could be highly beneficial in the design process.
View Article and Find Full Text PDFNat Commun
August 2025
Department of Physics, Loughborough University, Loughborough, UK.
Rapid development of memristive elements emulating biological neurons creates new opportunities for brain-like computation at low energy consumption. A first step toward mimicking complex neural computations is the analysis of single neurons and their characteristics. Here we measure and model spiking activity in artificial neurons built using diffusive memristors.
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