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1. Self-sustained green neuromorphic interfaces

   https://doi.org/10.1038/s41467-021-23744-2  

   Fu, Tianda ; Liu, Xiaomeng ; Fu, Shuai ; Woodard, Trevor ; Gao, Hongyan ; Lovley, Derek R. ; Yao, Jun ( December 2021 , Nature Communications)

   null (Ed.)

   Abstract Incorporating neuromorphic electronics in bioelectronic interfaces can provide intelligent responsiveness to environments. However, the signal mismatch between the environmental stimuli and driving amplitude in neuromorphic devices has limited the functional versatility and energy sustainability. Here we demonstrate multifunctional, self-sustained neuromorphic interfaces by achieving signal matching at the biological level. The advances rely on the unique properties of microbially produced protein nanowires, which enable both bio-amplitude (e.g., <100 mV) signal processing and energy harvesting from ambient humidity. Integrating protein nanowire-based sensors, energy devices and memristors of bio-amplitude functions yields flexible, self-powered neuromorphic interfaces that can intelligently interpret biologically relevant stimuli for smart responses. These features, coupled with the fact that protein nanowires are a green biomaterial of potential diverse functionalities, take the interfaces a step closer to biological integration. 

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2. 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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3. Nanoionics‐Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications

   https://doi.org/10.1002/aelm.201600510  

   Wang, Zhiyong ; Wang, Laiyuan ; Nagai, Masaru ; Xie, Linghai ; Yi, Mingdong ; Huang, Wei ( May 2017 , Advanced Electronic Materials)

   Memristors have been intensively studied in recent years as potential building blocks for the construction of versatile neuromorphic architectures. The prevalent developments focus on nanoionic devices due to their ease of reversible regulation, non‐volatility, scalability down to the atomic level, low‐energy consumption, nanosecond response times, sufficient yield and reliability, as well as their homologous regulatory mechanisms with biological synapses. However, despite their desirable structures and excellent properties, the previously reported properties of nanoionic devices are rather diverse and dissatisfactory for the delivery of reliable analog properties, which is the precondition for the imitation of brain‐like functions. Here, the general requirements of neuromorphic engineering in terms of device structure, characterization parameters, synaptic functions and device engineering are introduced. A critical overview of the proposed nanoionic mechanisms for memristive switching is given, focusing particularly on providing fundamental insights into the strategies for regulating the adaptive memristive characteristics of devices that resemble the behaviors of biological synapses, which is an element of neural networks. In addition, the research progress in active materials (e.g., oxides, polymers, small molecules, bio‐macromolecules and dopants), especially regarding the correlation among materials, is elaborated and a rational approach to provide insight into new design strategies is considered, and how to understand the memristor mechanisms and further achieve excellent memristive devices is discussed. Finally, the current challenges and several possible future research directions in this area are discussed.

    

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4. In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing

   https://doi.org/10.1038/s41467-022-32790-3  

   Lee, Doeon ; Park, Minseong ; Baek, Yongmin ; Bae, Byungjoon ; Heo, Junseok ; Lee, Kyusang ( September 2022 , Nature Communications)

   As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction.

    

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5. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits

   https://doi.org/10.1038/s41467-018-04482-4  

   Bayat, F. Merrikh ; Prezioso, M. ; Chakrabarti, B. ; Nili, H. ; Kataeva, I. ; Strukov, D. ( June 2018 , Nature Communications)

   The progress in the field of neural computation hinges on the use of hardware more efficient than the conventional microprocessors. Recent works have shown that mixed-signal integrated memristive circuits, especially their passive (0T1R) variety, may increase the neuromorphic network performance dramatically, leaving far behind their digital counterparts. The major obstacle, however, is immature memristor technology so that only limited functionality has been reported. Here we demonstrate operation of one-hidden layer perceptron classifier entirely in the mixed-signal integrated hardware, comprised of two passive 20 × 20 metal-oxide memristive crossbar arrays, board-integrated with discrete conventional components. The demonstrated network, whose hardware complexity is almost 10× higher as compared to previously reported functional classifier circuits based on passive memristive crossbars, achieves classification fidelity within 3% of that obtained in simulations, when using ex-situ training. The successful demonstration was facilitated by improvements in fabrication technology of memristors, specifically by lowering variations in theirI–Vcharacteristics.

    

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