Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. by Peng Liu, Ke Bo, Mingzhou Ding, Ruogu Fang

“…Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. …”
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Comparison of estimation capabilities of the artificial neural network with the wavelet neural network in lipase-catalyzed synthesis of triethanolamine-based esterquats cationic su... by Masoumi, Hamid Reza Fard, Basri, Mahiran, Kassim, Anuar, Abdullah, Dzulkefly Kuang, Abdollahi, Yadollah, Abd Gani, Siti Salwa

“…After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. …”
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Functioning of Declarative Memory: Intersection between Neuropsychology and Mathematics by Federica Doronzo, Gianvito Calabrese

“…The assembly of artificial neurons has the potential to clarify in detail the memory processes, the functioning of neural correlates and to carry out the mapping of the biological brain. …”
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A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways by Giovanni Maria Matrone, Eveline R. W. van Doremaele, Abhijith Surendran, Zachary Laswick, Sophie Griggs, Gang Ye, Iain McCulloch, Francesca Santoro, Jonathan Rivnay, Yoeri van de Burgt

“…Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. …”
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Neural encoding with unsupervised spiking convolutional neural network by Chong Wang, Hongmei Yan, Wei Huang, Wei Sheng, Yuting Wang, Yun-Shuang Fan, Tao Liu, Ting Zou, Rong Li, Huafu Chen

“…Despite recent breakthroughs in neural encoding using convolutional neural networks (CNNs) in fMRI studies, there remain critical gaps between the computational rules of traditional artificial neurons and real biological neurons. To address this issue, a spiking CNN (SCNN)-based framework is presented in this study to achieve neural encoding in a more biologically plausible manner. …”
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A state‐of‐the‐art survey of predicting students' performance using artificial neural networks by Wen Xiao, Juan Hu

“…The results show that: (1) objectives of most prediction model is the performance of learners on the program and course; (2) datasets used for training prediction model are collected from logs of the learning management system; (3) the most commonly used ANN is feedforward neural network; (4) researchers use stochastic gradient descent and Adam algorithm to optimizes the parameters in ANN and configure hyper parameters of ANN manually; (5) feature selection is not necessary because ANN can automatically adjust the weights of artificial neurons; and (6) ANN has better performance than the classical classifiers in predicting student performance. …”
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Event-driven adaptive optical neural network by Brückerhoff-Plückelmann, F, Bente, I, Becker, M, Vollmar, N, Farmakidis, N, Lomonte, E, Lenzini, F, Wright, CD, Bhaskaran, H, Salinga, M, Risse, B, Pernice, WHP

“…Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. …”

An overview of neural networks for medical image recognition by Berezovsky V.V., Vygovskaya N.V.

“…Neural networks, inspired by the functioning of the human brain, consist of interconnected artificial neurons organized in layers. Through the learning process, neural networks can analyze and classify medical images, enabling accurate diagnosis and treatment. …”
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Modeling Neurodegeneration in silico With Deep Learning by Anup Tuladhar, Anup Tuladhar, Jasmine A. Moore, Jasmine A. Moore, Jasmine A. Moore, Zahinoor Ismail, Zahinoor Ismail, Zahinoor Ismail, Zahinoor Ismail, Zahinoor Ismail, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert

“…We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. …”
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Optimal storage capacity of quantum Hopfield neural networks by Lukas Bödeker, Eliana Fiorelli, Markus Müller

“…As an example, we apply our method to an open-system quantum associative memory formed of interacting spin-1/2 particles realizing coupled artificial neurons. The system undergoes a Markovian time evolution resulting from a dissipative retrieval dynamics that competes with a coherent quantum dynamics. …”
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Learning in Convolutional Neural Networks Accelerated by Transfer Entropy by Adrian Moldovan, Angel Caţaron, Răzvan Andonie

“…Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. …”
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Artificial biphasic synapses based on nonvolatile phase‐change photonic memory cells by Zhou, W, Farmakidis, N, Li, X, Tan, J, Aggarwal, S, Feldmann, J, Brückerhoff-Plückelmann, F, Wright, CD, Pernice, WHP, Bhaskaran, H

“…Nonvolatile photonic memory cells are basic building blocks for neuromorphic hardware enabling the realization of all-optical synapses and artificial neurons. These devices commonly exploit chalcogenide phase-change materials, which are evanescently coupled to photonic waveguides, and provide fast write/erase speeds and large storage capacity. …”

Prioritization of Candidate Biomarkers for Degenerative Aortic Stenosis through a Systems Biology-Based In-Silico Approach by Nerea Corbacho-Alonso, Tamara Sastre-Oliva, Cecilia Corros, Teresa Tejerina, Jorge Solis, Luis F. López-Almodovar, Luis R. Padial, Laura Mourino-Alvarez, Maria G. Barderas

“…This work is focused on the study of previously defined biomarkers through systems biology and artificial neuronal networks to understand their potential role within aortic stenosis. …”
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Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning by Xiaoyun Yuan, Yong Wang, Zhihao Xu, Tiankuang Zhou, Lu Fang

“…Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. …”
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A review on device requirements of resistive random access memory (RRAM)-based neuromorphic computing by Jeong Hyun Yoon, Young-Woong Song, Wooho Ham, Jeong-Min Park, Jang-Yeon Kwon

“…Neuromorphic computing, which imitates biological neurons and processes data through parallel procedures between artificial neurons, is now regarded as a promising solution to address these restrictions. …”
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Novel Feature Extraction Methodology with Evaluation in Artificial Neural Networks Based Fingerprint Recognition System by Nihan Kahraman, Zehra Gulru Cam Taskiran, Murat Taskiran

“…The proposed method gives approximate position information of minutiae points with respect to the core point using a fairly simple, orientation map-based method that provides ease of operation, but with the use of artificial neurons with high fault tolerance, this method has been turned to an advantage. …”
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Printable electronics through direct ink writing by Santoso, Kevin Patrick

“…Varied resistance values were achieved by printing resistors with different dimensions, enabling the assessment of their predictability and reproducibility; These results were then used to form artificial neurons via hybrid 3D printing, which integrates conventional manufactured electronics with DIW and pick-and-place techniques. …”
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The Art of the Masses: Overviews on the Collective Visual Heritage through Convolutional Neural Networks by Pilar Rosado-Rodrigo, Ferran Reverter

“…One of the responsibilities of the contemporary artist is to adopt a position that will help to provide sense, to project meaning onto the accumulation of images that we are faced with. The artificial neuronal network ResNet-50 has been used in order to extract the visual characteristics of large sets of images from the internet. …”
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Learning curves for overparametrized deep neural networks: A field theory perspective by Omry Cohen, Or Malka, Zohar Ringel

“…Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning.…”
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Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model by Francesco Gentile, Matteo Ferro, Bartolomeo Della Ventura, Evelina La Civita, Antonietta Liotti, Michele Cennamo, Dario Bruzzese, Raffaele Velotta, Daniela Terracciano

“…Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. …”
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