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Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain’s intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions “on the fly” to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling, and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron’s state, its dynamics and its transfer function “on the fly.” This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster and using a more compact and energy-efficient network than a nonadaptive ANN. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modernmore »ANNs require large amounts of training data, energy, and chip area, and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning, compact architectures, energy-efficiency, fault-tolerance, and can lead to the realization of broader artificial intelligence.

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Ion selective electrode (ISE) sensors have been broadly applied for real-time in situ monitoring of ion concentrations in water environments. However, ISE sensors suffer from critical problems, such as ionophore leaching, water-penetration, poor electrochemical stability, and resulting short life spans. In this study, a template-guided membrane matrix immobilization strategy was pursued as a novel ISE sensor fabrication methodology to enhance its sensing characteristics and longevity. Specifically, nano-porous anodized aluminum oxide (AAO) was used as the template for an NH 4 + -specific ISE sensor. A nano-porous nickel mesh eventually replaced the template and formed a compact, high-surface juncture with the NH 4 + ion-selective membrane matrix. The resulting template-guided nano-mesh ISE (TN-ISE) sensor displayed enhanced electrochemical stability ( i.e. , capacitance increased by 50%, reading drift reduced by 75%) when compared to a regular single-wall carbon nanotube (SW-CNT) ISE sensor used as the standard. The interface between the nano-mesh electrode and the ion selective membrane matrix was compact enough to prevent water influx at the electrode interface. This minimized ionophore leaching and increased the mechanical integrity of the TN-ISE sensor. The practical advantages of the novel sensor were validated via long-term (360 hours) tests in real wastewater, returning a smallmore »average error of 1.28% over this time. The results demonstrate the feasibility of the template-guided nano-mesh design and fabrication strategy toward ISEs for long-term continuous monitoring of water or wastewater quality.« less

Neuromorphic computing is a promising candidate for beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. Lateral inhibition and winner-take-all (WTA) features play a crucial role in neuronal competition of the nervous system as well as neuromorphic hardwares. The domain wall - magnetic tunnel junction (DWMTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic lateral inhibition. In this paper we show that lateral inhibition parameters modulate the neuron firing statistics in a DW-MTJ neuron array, thus emulating soft-winner-take-all (WTA) and firing group selection.

Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal lateral inhibition feature, closely associated with the biological receptive eld, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic arti cial neuron device exhibiting intrinsic lateral inhibition. This work discusses lateral inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that lateral inhibition is eciently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).