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As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems. Here, we demonstrate an artificial neuron based on NbO x volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model. A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbO x memristor based neurons and nonvolatile TaO x memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems. Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate online learning in a monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor neurons and nonvolatile TaOx memristor synapses.

Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO 2 , where the high crystalline quality of VO 2 leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO 2 neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics. A highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, the authors report a spike-based neuromorphic perception system consisting of tunable and highly uniform artificial sensory neurons based on epitaxial VO 2 capable of hand gesture classification.

Memristors proposed by Leon Chua provide a new type of memory device for novel neuromorphic computing applications. However, the approaching of distinct multi‐intermediate states for tunable switching dynamics, the controlling of conducting filaments (CFs) toward high device repeatability and reproducibility, and the ability for large‐scale preparation devices, remain full of challenges. Here, we show that vertical‐organic‐nanocrystal‐arrays (VONAs) could make a way toward the challenges. The perfect one‐dimensional structure of the VONAs could confine the CFs accurately with fine‐tune resistance states in a broad range of 103 ratios. The availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely, and the analog switching characteristic of the memristors is to effectively imitate different kinds of synaptic plasticity, indicating their great potential in future applications. In this study, vertical‐organic‐nanocrystal‐arrays (VONAs) was developed to construct high‐performance memristors. The unique nanostructure of VONAs could confine conducting filaments accurately, showing fine resistance tuning in a broad ratio. The availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely, and their analog switching characteristic is effective to imitate different kinds of synaptic plasticity, indicating their great potential in future applications.

Exploiting the intrinsic nonlinearity in physical reservoirs, e.g., dopant‐atom networks, provides a new approach toward highly efficient computing such as feature projection and classification. In a recent study by Chen et al., the computational capability of dopant‐atom network was investigated and found to diminish as the signal‐to‐noise ratio (SNR) increased, indicating the existence of an optimal bias condition. Although high SNR is often pursued in signal processing, it shows that embracing noise in non‐conventional computing systems may lead to a leap in computing capacity. This work showcased that material or device physics in different domains offer valuable substrates for complex computing functions and high energy efficiency. Conventional computing platforms with nonlinear functionality usually require complicated circuits and control logic, thus consuming extensive chip area and energy. Exploration of the intrinsic nonlinearity in physical reservoirs provides a new approach toward building highly area‐ and energy‐efficient computing hardware with complex functionality. In particular, embracing noise in nonconventional computing systems may lead to a leap in computing capacity.

Ising machines are annealing processors that can solve combinatorial optimization problems via the physical evolution of the corresponding Ising graphs. Such machines are, however, typically restricted to solving problems with certain kinds of graph topology because the spin location and connections are fixed. Here, we report a universal Ising machine that supports arbitrary Ising graph topology with reasonable hardware resources using a coarse-grained compressed sparse row method to compress and store sparse Ising graph adjacency matrices. The approach, which we term interaction-centric storage, is suitable for any kind of Ising graph and reduces the memory scaling cost. We experimentally implement the Ising machine using compute-in-memory hardware based on a 40 nm resistive random-access memory arrays. We use the machine to solve max-cut and graph colouring problems, with the latter showing a 442–1,450 factor improvement in speed and 4.1 × 10 5 –6.0 × 10 5 factor reduction in energy consumption compared to a general-purpose graphics processing unit. We also use our Ising machine to solve a realistic electronic design automation problem—multiple patterning lithography layout decomposition—with 390–65,550 times speedup compared to the integer linear programming algorithm on a typical central processing unit. An Ising machine that uses a coarse-grained compressed sparse row method to store sparse Ising graph adjacency matrices can be implemented with compute-in-memory hardware based on a resistive random-access memory array to efficiently solve combinatorial optimization problems.

Memristive in-memory sorting has been proposed recently to improve hardware sorting efficiency. Using iterative in-memory min computations, data movements between memory and external processing units can be eliminated for improved latency and energy efficiency. However, the bit-traversal algorithm to search the min requires a large number of column reads on memristive memory. In this work, we propose a column-skipping algorithm with help of a near-memory circuit. Redundant column reads can be skipped based on recorded states for improved latency and hardware efficiency. To enhance the scalability, we develop a multi-bank management that enables column-skipping for dataset stored in different memristive memory banks. Prototype column-skipping sorters are implemented with a 1T1R memristive memory in 40nm CMOS technology. Experimented on a variety of sorting datasets, the length-1024 32-bit column-skipping sorter with state recording of 2 demonstrates up to 4.08x speedup, 3.14x area efficiency and 3.39x energy efficiency, respectively, over the latest memristive in-memory sorting.

Sorting is fundamental and ubiquitous in modern computing systems. Hardware sorting systems are built based on comparison operations with Von Neumann architecture, but their performance are limited by the bandwidth between memory and comparison units and the performance of complementary metal-oxide-semiconductor (CMOS) based circuitry. Sort-in-memory (SIM) based on emerging memristors is desired but not yet available due to comparison operations that are challenging to be implemented within memristive memory. Here we report fast and reconfigurable SIM system enabled by digit read (DR) on 1-transistor-1-resistor (1T1R) memristor arrays. We develop DR tree node skipping (TNS) that support variable data quantity and data types, and extend TNS with multi-bank, bit-slice and multi-level strategies to enable cross-array TNS (CA-TNS) for practical adoptions. Experimented on benchmark sorting datasets, our memristor-enabled SIM system presents up to 3.32x~7.70x speedup, 6.23x~183.5x energy efficiency improvement and 2.23x~7.43x area reduction compared with state-of-the-art sorting systems. We apply such SIM system for shortest path search with Dijkstra's algorithm and neural network inference with in-situ pruning, demonstrating the capability in solving practical sorting tasks and the compatibility in integrating with other compute-in-memory (CIM) schemes. The comparison-free TNS/CA-TNS SIM enabled by memristors pushes sorting into a new paradigm of sort-in-memory for next-generation sorting systems.

Previous studies have demonstrated the effects of different afferent fibers on electroacupuncture （EA）- induced analgesia. However, contributions of functional receptors expressed on afferent fibers to the EA analgesia remain unclear. This study investigates the roles of acid-sensing ion channel 3 （ASIC3） and transient receptor potential vanifioid 1 （TRPV1） receptors in EA-induced segmental and systemic analgesia. Effects of EA at acupoint ST36 with different intensities on the C-fiber reflex and mechanical and thermal pain thresholds were measured among the ASIC3-/-, TRPV1-/-, and C57BL/6 mice. Compared with C57BL/6 mice, the ipsilateral inhibition of EA with 0.8 C-fiber threshold （0.8Tc） intensity on C-fiber reflex was markedly reduced in ASIC3-/- mice, whereas the bilateral inhibition of 1.0 and 2.0Tc EA was significantly decreased in TRPV1-/- mice. The segmental increase in pain thresholds induced by 0.3 mA EA was significantly reduced in ASIC3-/- mice, whereas the systemic enhancement of 1.0 mA EA was markedly decreased in TRPV1-/- mice. Thus, segmental analgesia of EA with lower intensity is partially mediated by ASIC3 receptor on Aβ-fiber, whereas systemic analgesia induced by EA with higher intensity is more likely induced by TRPV1 receptor on Aδ- and C-fibers.

The crystal structures would directly affect the physical and chemical properties of the surface of the material, and would thus influence the catalytic activity of the material. α-MnO 2 , β-MnO 2 and γ-MnO 2 nanorods with the same morphology yet different crystal structures were prepared and tested as oxidase mimics using 3,3’,5,5’-tetramethylbenzidine (TMB) as the substrate. β-MnO 2 that exhibited the highest activity had a catalytic constant of 83.75 μmol·m −2 ·s −1 , 2.7 and 19.0 times of those of α-MnO 2 and γ-MnO 2 (30.91 and 4.41 μmol·m −2 ·s −1 ), respectively. The characterization results showed that there were more surface hydroxyls as well as more Mn 4+ on the surface of the β-MnO 2 nanorods. The surface hydroxyls were conducive to the oxidation reaction, while Mn 4+ was conducive to the regeneration of surface hydroxyls. The synergistic effect of the two factors significantly improved the activity of β-MnO 2 oxidase mimic. Using β-MnO 2 , a β-MnO 2 -TMB-GSH system was established to detect the content of glutathione (GSH) rapidly and sensitively by colorimetry. This method had a wide detection range (0.11-45 μM) and a low detection limit (0.1 μM), and had been successfully applied to GSH quantification in human serum samples.

Regulatory B cells (Breg) critically orchestrate inflammatory resolution and tissue repair. This study investigates the therapeutic potential of transforming growth factor (TGF)-β1-producing Bregs in ventilator-induced lung injury (VILI), leveraging biomimetic nanotechnology to overcome limitations of conventional cytokine delivery. We engineered macrophage-derived microvesicle-encapsulated nanoparticles (TMNP) for pH-responsive, spatiotemporally controlled TGF-β1 release. Therapeutic efficacy was evaluated in a murine VILI model through longitudinal immunophenotyping, histopathology, and cytokine profiling at post-ventilation days 1 and 10 (PV1d, PV10d). VILI triggered biphasic pulmonary Breg expansion (PV1d: 7.83-fold . controls, < 0.001; PV10d resurgence) coinciding with peak injury. TMNP administration induced sustained TGF-β1 bioavailability (PV10d: 3.6-fold . free cytokine, < 0.001), attenuating histopathology (22.5% reduction in alveolar hemorrhage, < 0.01) and suppressing IL-6/TNF-α ( < 0.01). Treatment concomitantly expanded Breg populations and modulated T cell subset. TMNP orchestrates Breg-mediated immunoresolution through precision cytokine delivery and lymphocyte modulation, enabling dual-phase protection against ventilation-associated immunopathology. This paradigm represents a transformative approach for acute respiratory distress management.