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1. Constructing artificial neurons with functional parameters comprehensively matching biological values

   https://doi.org/10.1038/s41467-025-63640-7  

   Fu, Shuai; Gao, Hongyan; Wang, Siqi; Wang, Xiaoyu; Woodard, Trevor; Wang, Zhien; Kong, Jing; Lovley, Derek R; Yao, Jun (September 2025, Nature Communications)

   The efficient signal processing in biosystems is largely attributed to the powerful constituent unit of a neuron, which encodes and decodes spatiotemporal information using spiking action potentials of ultralow amplitude and energy. Constructing devices that can emulate neuronal functions is thus considered a promising step toward advancing neuromorphic electronics and enhancing signal flow in bioelectronic interfaces. However, existent artificial neurons often have functional parameters that are distinctly mismatched with their biological counterparts, including signal amplitude and energy levels that are typically an order of magnitude larger. Here, we demonstrate artificial neurons that not only closely emulate biological neurons in functions but also match their parameters in key aspects such as signal amplitude, spiking energy, temporal features, and frequency response. Moreover, these artificial neurons can be modulated by extracellular chemical species in a manner consistent with neuromodulation in biological neurons. We further show that an artificial neuron can connect to a biological cell to process cellular signals in real-time and interpret cell states. These results advance the potential for constructing bio-emulated electronics to improve bioelectronic interface and neuromorphic integration. 

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2. Recent progress in bio-voltage memristors working with ultralow voltage of biological amplitude

   https://doi.org/10.1039/D2NR06773K  

   Fu, Tianda; Fu, Shuai; Yao, Jun (March 2023, Nanoscale)

   Neuromorphic systems built from memristors that emulate bioelectrical information processing in a brain may overcome limits in traditional computing architectures. However, functional emulation alone may still not attain all the merits of bio-computation, which uses action potentials of 50-120 mV at least 10-time lower than signal amplitude in conventional electronics to achieve extraordinary power efficiency and effective functional integration. Reducing the functional voltage in memristors to this biological amplitude thus can advance neuromorphic engineering and bio-emulated integration. This review aims to provide a timely update on the effort and progress in this burgeoning direction, covering aspects in device material composition, performance, working mechanism, and potential application. 

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3. Bioinspired bio-voltage memristors

   https://doi.org/10.1038/s41467-020-15759-y  

   Fu, T; Liu, X; Gao, H; Ward, J. E; Liu, X; Yin, B; Wang, Z; Zhuo, Y; Walker, D; Yang, J. J; et al (April 2020, Nature communications)

   null (Ed.)

   Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated. 

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4. Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

   https://doi.org/10.1038/s41467-022-32078-6  

   Kireev, Dmitry; Liu, Samuel; Jin, Harrison; Patrick Xiao, T.; Bennett, Christopher H.; Akinwande, Deji; Incorvia, Jean Anne (December 2022, Nature Communications)

   Abstract CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks. 

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5. From memristive devices to neuromorphic systems

   https://doi.org/10.1063/5.0133044  

   Huang, Yi; Kiani, Fatemeh; Ye, Fan; Xia, Qiangfei (March 2023, Applied Physics Letters)

   Progress in hardware and algorithms for artificial intelligence (AI) has ushered in large machine learning models and various applications impacting our everyday lives. However, today's AI, mainly artificial neural networks, still cannot compete with human brains because of two major issues: the high energy consumption of the hardware running AI models and the lack of ability to generalize knowledge and self-adapt to changes. Neuromorphic systems built upon emerging devices, for instance, memristors, provide a promising path to address these issues. Although innovative memristor devices and circuit designs have been proposed for neuromorphic computing and applied to different proof-of-concept applications, there is still a long way to go to build large-scale low-power memristor-based neuromorphic systems that can bridge the gap between AI and biological brains. This Perspective summarizes the progress and challenges from memristor devices to neuromorphic systems and proposes possible directions for neuromorphic system implementation based on memristive devices. 

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