Bursting dynamics in a spiking neuron with a memristive voltage-gated channel by Jiaming Wu, Kang Wang, Olivier Schneegans, Pablo Stoliar, Marcelo Rozenberg

“…We introduce a voltage-gated conductance model for an artificial neuron that exhibits tonic, fast, and two types of intrinsic burst spiking. …”
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Emulating Nociceptive Receptor and LIF Neuron Behavior via ZrOx‐based Threshold Switching Memristor by Jia‐He Yang, Shi‐Cheng Mao, Kuan‐Ting Chen, Jen‐Sue Chen

“…Additionally, with the change in the strength of the external stimulus, the artificial neuron is also built by emulating “all‐or‐nothing,” “threshold‐driven‐spiking,” and “strength‐modulated” characteristics. …”
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Solar PV power forecasting considering missing data by Zhai, Chengrui

“…This paper proposes a fresh new online training model based on the hybrid Artificial Neuron Network (ANN) machine learning to address the incomplete or missing data issue. …”
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Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer. by Anh Quan Ngo, Linh Quy Nguyen, Van Quan Tran

“…Four models including Random Forest (RF), Artificial Neuron Network (ANN), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) are built. …”
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Multi-state MRAM cells for hardware neuromorphic computing by Piotr Rzeszut, Jakub Chȩciński, Ireneusz Brzozowski, Sławomir Ziȩtek, Witold Skowroński, Tomasz Stobiecki

“…Multi-cells are connected to a CMOS-based summing amplifier and a sigmoid function generator, forming an artificial neuron. The operation of the designed network is tested using a recognition of hand-written digits in 20 $$\times $$ × 20 pixels matrix and shows detection ratio comparable to the software algorithm, using weights stored in a multi-cell consisting of four MTJs or more. …”
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Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer by Anh Quan Ngo, Linh Quy Nguyen, Van Quan Tran

“…Four models including Random Forest (RF), Artificial Neuron Network (ANN), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) are built. …”
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Multivariate analysis of socioeconomic profiles in the Ruhr area, Germany by Janka Lengyel, Stéphane Roux, Seraphim Alvanides

“…The multivariate analysis was carried out at this scale using Self-Organizing Maps (SOM), an artificial neuron network, which uses an unsupervised learning mechanism for projecting multidimensional data in a low (in our case two) dimensional space. …”
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Superconducting nanowire electronics for alternative computing by Toomey, Emily.

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Patterning and manipulating microparticles into a three-dimensional matrix using standing surface acoustic waves by Nguyen, Tan Dai, Tran, Van Thai, Fu, Yong Qing, Du, Hejun

“…Our method has great potential for acoustofluidic applications, building the large-scale structures associated with biological objects and artificial neuron networks.…”
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Multiplexed Complementary Signal Transmission for a Self‐Regulating Artificial Nervous System by Young Jin Choi, Dong Gue Roe, Yoon Young Choi, Seongchan Kim, Sae Byeok Jo, Hwa Sung Lee, Do Hwan Kim, Jeong Ho Cho

“…As a proof of concept, a feedback‐based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. …”
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Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea by Kyo Beom Han, Jaesung Jung, Byung O Kang

“…To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. …”
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Application of Artificial Neural Network for Prediction of Key Indexes of Corn Industrial Drying by Considering the Ambient Conditions by Bin Li, Chengjie Li, Junying Huang, Changyou Li

“…An optimal architecture of 9-2-12-3 artificial neuron network model was developed and the best prediction performance of the artificial neural network (ANN) model were found at a training epoch number of 30, and a momentum coefficient of 0.4, where the coefficient of determination of moisture content, exergy efficiency of heat exchanger, and the specific recovered radiant energy, respectively are 0.998, 0.992, and 0.980, indicating that the model has an excellent prediction performance and can be used in engineering practice.…”
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Evidence-Based Regularization for Neural Networks by Giuseppe Nuti, Andreea-Ingrid Cross, Philipp Rindler

“…This, at the single neuron level, is equivalent to ensuring that both sides of the separating hyperplane (for a standard artificial neuron) have a minimum number of data points, noting that these points need not belong to the same class for the inner layers. …”
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Superconducting Bio-Inspired Au-Nanowire-Based Neurons by Olga V. Skryabina, Andrey E. Schegolev, Nikolay V. Klenov, Sergey V. Bakurskiy, Andrey G. Shishkin, Stepan V. Sotnichuk, Kirill S. Napolskii, Ivan A. Nazhestkin, Igor I. Soloviev, Mikhail Yu. Kupriyanov, Vasily S. Stolyarov

“…The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored theoretically. …”
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A Novel Interannual Rainfall Runoff Equation Derived from Ol’Dekop’s Model Using Artificial Neural Networks by Omar Mimeche, Amir Aieb, Antonio Liotta, Khodir Madani

“…This is a new improvement of Ol’Dekop’s equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANN_s), namely by ANN₁ and ANN₂ sub-models. …”
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Exploring artificial neural network approach and RSM modeling in the prediction of CO2 capture using carbon molecular sieves by Ahad Ghaemi, Mohsen Karimi Dehnavi, Zohreh Khoshraftar

“…In this work, adsorption and reduction of CO2 by carbon molecular sieves (CMS) was modeled using response surface methodology (RSM) and artificial neuron networks (ANNs). The CO2 adsorption experiments were carried out at temperature in range of 20–80 °C, pressure in range of 2–10 bar, and time in range of 0–1800 sec. 311 experimental data points of CO2 adsorption on the carbon molecular sieves are applied in the development of an ANN model. …”
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Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder me... by Alam, Mohammad Azad, Ya, Hamdan H., Mohammad Azeem, Mohammad Yusuf, Soomro, Imtiaz Ali, Masood, Faisal, Shozib, Imtiaz Ahmed, Salit, M. Sapuan, Akhter, Javed

“…Furthermore, an artificial neuron network (ANN) model was developed to predict the crystallite size and lattice strain of the synthesized composites. …”

Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration by Sijie Liu, Xinyu Liu, Pei Lu

“…This is because the mechanism of interference factors is complex and there is often insufficient prior knowledge on a specific sensor type. Although Artificial-Neuron-Net-based (ANN-based) methods ignore the complex conditions above, they also have problems regarding generalization, interpretability, and calculation cost. …”
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In Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual‐Mode Memristor by Linbo Shan, Zongwei Wang, Lin Bao, Shengyu Bao, Yabo Qin, Yaotian Ling, Guandong Bai, John Robertson, Yimao Cai, Ru Huang

“…Artificial neurons are the fundamental elements in neuromorphic computing systems. …”
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Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm by Ciyun Lin, Hui Liu, Dayong Wu, Bowen Gong

“…Based on the point cloud classification results, the traffic objects (pedestrians and vehicles) and their surrounding information can be easily identified from an individual frame of the point cloud. We proposed an artificial neuron network (ANN)-based model to improve the adaptability of the algorithm in dealing with the road gradient and LiDAR-employing inclination. …”
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