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Journal articles on the topic 'Neural structures'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Johnson, Don H. "Neural Population Structures and Consequences for Neural Coding." Journal of Computational Neuroscience 16, no. 1 (2004): 69–80. http://dx.doi.org/10.1023/b:jcns.0000004842.04535.7c.

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2

Zhou, Ding-Xuan. "Deep distributed convolutional neural networks: Universality." Analysis and Applications 16, no. 06 (2018): 895–919. http://dx.doi.org/10.1142/s0219530518500124.

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Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networ

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3

Seoane, Luís F. "Fate of Duplicated Neural Structures." Entropy 22, no. 9 (2020): 928. http://dx.doi.org/10.3390/e22090928.

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Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory wo

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4

Zanuttigh, Barbara, Sara Mizar Formentin, and Jentsje W. Van der Meer. "ADVANCES IN MODELLING WAVE-STRUCTURE INTERACTION THROUGH ARTIFICIAL NEURAL NETWORKS." Coastal Engineering Proceedings 1, no. 34 (2014): 69. http://dx.doi.org/10.9753/icce.v34.structures.69.

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5

Friederici, Angela Dorkas, Jörg Bahlmann, Roland Friedrich, and Michiru Makuuchi. "The Neural Basis of Recursion and Complex Syntactic Hierarchy." Biolinguistics 5, no. 1-2 (2011): 087–104. http://dx.doi.org/10.5964/bioling.8833.

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Language is a faculty specific to humans. It is characterized by hierarchical, recursive structures. The processing of hierarchically complex sentences is known to recruit Broca’s area. Comparisons across brain imaging studies investigating similar hierarchical structures in different domains revealed that complex hierarchical structures that mimic those of natural languages mainly activate Broca’s area, that is, left Brodmann area (BA) 44/45, whereas hierarchically structured mathematical formulae, moreover, strongly recruit more anteriorly located region BA 47. The present results call for a

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6

Pedrycz, Witold. "Neural Structures of Fuzzy Decision-Making." Journal of Intelligent and Fuzzy Systems 2, no. 2 (1994): 161–78. http://dx.doi.org/10.3233/ifs-1994-2205.

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7

Mahadevan, Indu, and Indira Ghosh. "Analysis ofE.colipromoter structures using neural networks." Nucleic Acids Research 22, no. 11 (1994): 2158–65. http://dx.doi.org/10.1093/nar/22.11.2158.

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8

Hadi, Muhammad N. S. "Neural networks applications in concrete structures." Computers & Structures 81, no. 6 (2003): 373–81. http://dx.doi.org/10.1016/s0045-7949(02)00451-0.

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9

Boozarjomehry, R. B., and W. Y. Svrcek. "Automatic design of neural network structures." Computers & Chemical Engineering 25, no. 7-8 (2001): 1075–88. http://dx.doi.org/10.1016/s0098-1354(01)00680-9.

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10

Anguita, Davide, Giancarlo Parodi, and Rodolfo Zunino. "Neural structures for visual motion tracking." Machine Vision and Applications 8, no. 5 (1995): 275–88. http://dx.doi.org/10.1007/bf01211489.

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11

Wan, Yi-Kwei, J. Ghaboussi, P. Venini, and K. Nikzad. "Control of structures using neural networks." Smart Materials and Structures 4, no. 1A (1995): A149—A157. http://dx.doi.org/10.1088/0964-1726/4/1a/018.

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12

Anguita, Davide, Giancarlo Parodi, and Rodolfo Zunino. "Neural structures for visual motion tracking." Machine Vision and Applications 8, no. 5 (1995): 275–88. http://dx.doi.org/10.1007/s001380050009.

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13

Felten, David L., and Suzanne Y. Felten. "Immune interactions with specific neural structures." Brain, Behavior, and Immunity 1, no. 4 (1987): 279–83. http://dx.doi.org/10.1016/0889-1591(87)90030-4.

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14

KRASILENKO, VLADIMIR, NATALIYA YURCHUK, and Diana NIKITOVICH. "DESIGN AND SIMULATION OF NEURON-EQUIVALENTORS ARRAY FOR CREATION OF SELF-LEARNING EQUIVALENT-CONVOLUTIONAL NEURAL STRUCTURES (SLECNS)." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 297, no. 3 (2021): 58–69. http://dx.doi.org/10.31891/2307-5732-2021-297-3-58-69.

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In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and DNNS), for associative memory models, clustering, and pattern recognition. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications is in several times quantity of neurons. Such neural paradigms are very perspective for processing, clustering, recognition, storing large

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15

Akiyama, Manato, Yasubumi Sakakibara, and Kengo Sato. "Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with Pseudoknots." Genes 13, no. 11 (2022): 2155. http://dx.doi.org/10.3390/genes13112155.

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Existing approaches to predicting RNA secondary structures depend on how the secondary structure is decomposed into substructures, that is, the architecture, to define their parameter space. However, architecture dependency has not been sufficiently investigated, especially for pseudoknotted secondary structures. In this study, we propose a novel algorithm for directly inferring base-pairing probabilities with neural networks that do not depend on the architecture of RNA secondary structures, and then implement this approach using two maximum expected accuracy (MEA)-based decoding algorithms:

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16

MEDINA, JOSEP RAMON, and JORGE MOLINES. "ROUGHNESS FACTOR IN OVERTOPPING ESTIMATION." Coastal Engineering Proceedings, no. 35 (June 23, 2017): 7. http://dx.doi.org/10.9753/icce.v35.structures.7.

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The roughness factor (Î³f) is a key variable to estimate wave overtopping discharge on mound breakwaters. In this study, the Î³f is re-calibrated using a dataset extracted from the CLASH database. Compared to previous roughness factors calibrated using less restrictive data, overtopping estimators with a few explanatory variables showed variations up to 15% in the 50% percentile of Î³f. On the contrary, the CLASH neural network overtopping predictor showed insignificant variations in the roughness factor, since it is less sensitive to the variability in the data used for calibration. The confi

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17

Lieberman, Philip. "Neuroanatomical structures and segregated circuits." Behavioral and Brain Sciences 19, no. 4 (1996): 641. http://dx.doi.org/10.1017/s0140525x00043417.

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AbstractSegregated neural circuits that effect particular domain-specific behaviors can be differentiated from neuroanatomical structures implicated in many different aspects of behavior. The basal ganglionic components of circuits regulating nonlinguistic motor behavior, speech, and syntax all function in a similar manner. Hence, it is unlikely that special properties and evolutionary mechanisms are associated with the neural bases of human language.

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18

Ran, Jingyang, and Tiecheng Zhang. "Fixed-time synchronization control of fuzzy inertial neural networks with mismatched parameters and structures." AIMS Mathematics 9, no. 11 (2024): 31721–39. http://dx.doi.org/10.3934/math.20241525.

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<p>This research addressed the issue of fixed-time synchronization between random neutral-type fuzzy inertial neural networks and non-random neutral-type fuzzy inertial neural networks. Notably, it should be emphasized that the parameters of the drive and reaction systems did not correspond. Initially, additional free parameters were introduced to reduce the order of the error system. Subsequently, considering the influence of memory on system dynamics, a piecewise time-delay fixed time controller was developed to compensate for the influence of the time delay on the system. Utilizing st

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19

Zhou, Yuxiang, Xiang Cai, Qingfeng Zhao, Zhoufang Xiao, and Gang Xu. "Quadrilateral Mesh Generation Method Based on Convolutional Neural Network." Information 14, no. 5 (2023): 273. http://dx.doi.org/10.3390/info14050273.

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The frame field distributed inside the model region characterizes the singular structure features inside the model. These singular structures can be used to decompose the model region into multiple quadrilateral structures, thereby generating a block-structured quadrilateral mesh. For the generation of block-structured quadrilateral mesh for two-dimensional geometric models, a convolutional neural network model is proposed to identify the singular structure inside the model contained in the frame field. By training the network model with a large number of model region decomposition data obtain

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20

Hegel, Lena, Andrea Kauth, Karsten Seidl, and Sven Ingebrandt. "Self-Assembling Flexible 3D-MEAs for Cortical Implants." Current Directions in Biomedical Engineering 7, no. 2 (2021): 359–62. http://dx.doi.org/10.1515/cdbme-2021-2091.

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Abstract Flexible Multi Electrode Arrays (MEAs) for neural interfacing reduce the mechanical mismatch between the soft brain tissue and the electrode arrays allowing accurate signal recordings and neural stimulation while reducing inflammatory responses. Many standard manufacturing processes of MEAs are designed for planar structures and the production of three-dimensional structures is challenging. In the present study, shaft structures with one to two circular gold microelectrodes (10 - 20 μm), each on a base polyimide (PI) substrate, were investigated. We describe a fabrication method, with

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21

Choe, Yoonsuck. "How neural is the neural blackboard architecture?" Behavioral and Brain Sciences 29, no. 1 (2006): 72–73. http://dx.doi.org/10.1017/s0140525x06249021.

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The target article does not provide insight into how the proposed neural blackboard architecture can be mapped to known neural structures in the brain. There are theories suggesting that the thalamus may be a good candidate. However, the experimental evidence suggests that the cortex may be involved (if in fact the blackboard is implemented in the brain). Issues arising from such a mapping will be discussed.

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22

Shang, Zhiyuan, Zhonghua Yao, Jian Liu, et al. "Automated Classification of Auroral Images with Deep Neural Networks." Universe 9, no. 2 (2023): 96. http://dx.doi.org/10.3390/universe9020096.

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Terrestrial auroras are highly structured that visualize the perturbations of energetic particles and electromagnetic fields in Earth’s space environments. However, the identification of auroral morphologies is often subjective, which results in confusion in the community. Automated tools are highly valuable in the classification of auroral structures. Both CNNs (convolutional neural networks) and transformer models based on the self-attention mechanism in deep learning are capable of extracting features from images. In this study, we applied multiple algorithms in the classification of aurora

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23

Ahmadi, N., R. Kamyab Moghadas, and A. Lavaei. "Dynamic Analysis of Structures Using Neural Networks." American Journal of Applied Sciences 5, no. 9 (2008): 1251–56. http://dx.doi.org/10.3844/ajassp.2008.1251.1256.

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24

Wanderley, Diego, Carlos Ferreira, Aurélio Campilho, and Jorge Silva. "Ovarian Structures Detection using Convolutional Neural Networks." Procedia Computer Science 196 (2022): 542–49. http://dx.doi.org/10.1016/j.procs.2021.12.047.

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25

Ciskowski, P., and E. Rafajlowicz. "Context-Dependent Neural Nets—Structures and Learning." IEEE Transactions on Neural Networks 15, no. 6 (2004): 1367–77. http://dx.doi.org/10.1109/tnn.2004.837839.

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26

Tremblay, G. F. "Orgaaization of Neural Networks. Structures and Models." Neurology 40, no. 10 (1990): 1640. http://dx.doi.org/10.1212/wnl.40.10.1640-a.

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27

Batalama, S. N., A. G. Koyiantis, P. Papantoni-Kazakos, and D. Kazakos. "Feedforward neural structures in binary hypothesis testing." IEEE Transactions on Communications 41, no. 7 (1993): 1047–62. http://dx.doi.org/10.1109/26.231936.

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28

Young, Hunter K., Xiaodong Tan, Nan Xia, and Claus-Peter Richter. "Target structures for cochlear infrared neural stimulation." Neurophotonics 2, no. 2 (2015): 025002. http://dx.doi.org/10.1117/1.nph.2.2.025002.

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29

Guyenet, Patrice G. "Neural structures that mediate sympathoexcitation during hypoxia." Respiration Physiology 121, no. 2-3 (2000): 147–62. http://dx.doi.org/10.1016/s0034-5687(00)00125-0.

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30

Ghaboussi, Jamshid, and Abdolreza Joghataie. "Active Control of Structures Using Neural Networks." Journal of Engineering Mechanics 121, no. 4 (1995): 555–67. http://dx.doi.org/10.1061/(asce)0733-9399(1995)121:4(555).

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31

van der Velde, Frank, and Marc de Kamps. "From neural dynamics to true combinatorial structures." Behavioral and Brain Sciences 29, no. 1 (2006): 88–104. http://dx.doi.org/10.1017/s0140525x06399025.

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Various issues concerning the neural blackboard architectures for combinatorial structures are discussed and clarified. They range from issues related to neural dynamics, the structure of the architectures for language and vision, and alternative architectures, to linguistic issues concerning the language architecture. Particular attention is given to the nature of true combinatorial structures and the way in which information can be retrieved from them in a productive and systematic manner.

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32

Ferraro, Mario, and Terry Caelli. "Neural computations of algebraic and geometrical structures." Neural Networks 11, no. 4 (1998): 699–707. http://dx.doi.org/10.1016/s0893-6080(97)00152-4.

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33

Neukart, Florian, and Sorin-Aurel Morar. "Operations on Quantum Physical Artificial Neural Structures." Procedia Engineering 69 (2014): 1509–17. http://dx.doi.org/10.1016/j.proeng.2014.03.148.

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34

Adeli, Hojjat, and Hyo Seon Park. "Optimization of space structures by neural dynamics." Neural Networks 8, no. 5 (1995): 769–81. http://dx.doi.org/10.1016/0893-6080(95)00026-v.

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35

Yen, G. G. "Autonomous neural control in flexible space structures." Control Engineering Practice 3, no. 4 (1995): 471–83. http://dx.doi.org/10.1016/0967-0661(95)00019-q.

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36

Zaidi, Q., J. Victor, J. McDermott, M. Geffen, S. Bensmaia, and T. A. Cleland. "Perceptual Spaces: Mathematical Structures to Neural Mechanisms." Journal of Neuroscience 33, no. 45 (2013): 17597–602. http://dx.doi.org/10.1523/jneurosci.3343-13.2013.

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37

Czibula, Gabriela, Istvan Gergely Czibula, and Radu Dan Găceanu. "Intelligent data structures selection using neural networks." Knowledge and Information Systems 34, no. 1 (2011): 171–92. http://dx.doi.org/10.1007/s10115-011-0468-3.

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38

Rudolph-Lilith, Michelle, and Lyle E. Muller. "Aspects of randomness in neural graph structures." Biological Cybernetics 108, no. 4 (2014): 381–96. http://dx.doi.org/10.1007/s00422-014-0606-6.

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39

Combettes, Patrick L., and Jean-Christophe Pesquet. "Deep Neural Network Structures Solving Variational Inequalities." Set-Valued and Variational Analysis 28, no. 3 (2020): 491–518. http://dx.doi.org/10.1007/s11228-019-00526-z.

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40

LE RICHE, R., D. GUALANDRIS, J. J. THOMAS, and F. HEMEZ. "NEURAL IDENTIFICATION OF NON-LINEAR DYNAMIC STRUCTURES." Journal of Sound and Vibration 248, no. 2 (2001): 247–65. http://dx.doi.org/10.1006/jsvi.2001.3737.

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41

Templier, Paul. "Leveraging Structures in Evolutionary Neural Policy Search." ACM SIGEVOlution 18, no. 1 (2025): 1–3. https://doi.org/10.1145/3733097.3733101.

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Training agents to perform complex tasks like driving a car, mastering a video game, or controlling a robot to walk presents a significant challenge when expert demonstrations are not available. In nature, complex behaviors and characteristics can emerge through evolution, as animals adapt to their environments and problems over generations.

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42

López-Ojeda, Wilfredo, and Robin A. Hurley. "Neuroanatomic Structures and Neural Circuits of Habits." Journal of Neuropsychiatry and Clinical Neurosciences 37, no. 3 (2025): A4–198. https://doi.org/10.1176/appi.neuropsych.20250046.

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43

SHAMS, SOHEIL, and JEAN-LUC GAUDIOT. "PARALLEL IMPLEMENTATIONS OF NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 02, no. 04 (1993): 557–81. http://dx.doi.org/10.1142/s0218213093000266.

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Neural network models have attracted much attention recently by demonstrating their potential at being an effective paradigm for implementing human-like intelligent processing. Neural network models, applied to “real-world” problems, demand high processing rates. Fortunately, neural network models contain several inherently parallel computing structures which can be utilized for high throughput implementations on parallel processing architectures. In this paper we describe the basic computational requirements and the various interconnection structures that are used by neural network models. A

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44

Osipov, Vasiliy, and Dmitriy Miloserdov. "Neural network event forecasting for robots with continuous training." Information and Control Systems, no. 5 (October 20, 2020): 33–42. http://dx.doi.org/10.31799/1684-8853-2020-5-33-42.

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Introduction: High hopes for a significant expansion of human capabilities in various fields of activity are pinned on the creation and use of highly intelligent robots. To achieve this level of robot intelligence, it is necessary to successfully solve the problems of predicting the external environment and the state of the robots themselves. Solutions based on recurrent neural networks with controlled elements are promising neural network forecasting systems. Purpose: Search for appropriate neural network structures for predicting events. Development of approaches to controlling the associati

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45

Shi, Ting, Wu Yang, and Junfei Qiao. "Research on Nonlinear Systems Modeling Methods Based on Neural Networks." Journal of Physics: Conference Series 2095, no. 1 (2021): 012037. http://dx.doi.org/10.1088/1742-6596/2095/1/012037.

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Abstract Nonlinear systems widely exist in all fields of industrial production and are difficult to model because of complex non-linearity. Neural network is widely used in process prediction, fault detection and fault diagnosis of modern industry because of the nonlinear fitting ability. Due to various structures, there exists diversity in the performance of neural networks. However, only the appropriate network can improve the efficiency and safety in modelling nonlinear industrial process, which requires full consideration of the structure of neural network. In this study, several typical s

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46

Boronina, Anna, Vladimir Maksimenko, and Alexander E. Hramov. "Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data." Mathematics 11, no. 11 (2023): 2515. http://dx.doi.org/10.3390/math11112515.

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Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets. However, the direct application of traditional deep learning algorithms, such as Convolutional Neural Networks (CNNs), is limited as they are designed for regular Euclidean data like 2D grids and 1D sequences. In contrast, graph-structured data are in a non-Euclidean form. Graph Neural Networks (GNNs) are specifically designed to handle non-Euclidean data and make predictions based on connectivity rather than

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47

Vynnykov, Yurii, Maksym Kharchenko, Svitlana Manhura, Aleksej Aniskin, and Andrii Manhura. "Neural network analysis of safe life of the oil and gas industrial structures." Mining of Mineral Deposits 18, no. 1 (2024): 37–44. http://dx.doi.org/10.33271/mining18.01.037.

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Purpose is to study safe life of industrial (metal) structures under long-time operation in the corrosive-active media of oil and gas wells with the help of neural network analysis. Methods. The MATLAB system (MATrix LABoratory) was selected as the tool environment for interface modelling; the system is developed by Math Works Inc. and is a high-level programming language for technical computations. Of the three existing learning paradigms, we used the “with teacher” learning process, as we believed that a neural network had correct answers (network outputs) for each input example. The coeffic

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48

Díaz-Carrasco, Pilar, Jorge Molines, Esther Gómez-Martín, and Josep R. Medina. "NEW SIMPLE AND EXPLICIT FORMULA FOR WAVE REFLECTION ON MOUND BREAKWATERS USING NEURAL NETWORK MODELING." Coastal Engineering Proceedings, no. 38 (May 29, 2025): 13. https://doi.org/10.9753/icce.v38.structures.13.

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Wave reflection from breakwaters may influence beaches and the entrance of harbors due to dangerous wave conditions and may compromise the structure stability due to the induced scour at the structure toe. These problems will have a greater impact in the future due to climate change, sea level rise and stronger wave storms (Camus et al., 2019). The challenges of protecting coasts and harbors from the effects of global warming and the correct estimation of reflected energy from coastal structures demand more precise and easy-to-apply design formulas for mound breakwaters. Hence, the main object

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49

Dementev, Vitaliy E., Ruslan A. Savinov, Marat N. Suetin, and Anatoliy G. Podloboshnikov. "THE SYSTEM OF DAMAGE RECOGNITION IN METAL STRUCTURES." Автоматизация процессов управления 2, no. 64 (2021): 40–45. http://dx.doi.org/10.35752/1991-2927-2021-2-64-40-45.

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The article deals with a system developed on the basis of a neural network approach that allows the system to detect the visual defects and damage of infrastructure facilities using photo/video data processing. The photo/video images of structural railroad bridge members were used as training data for the convolutional neural network U-Net. The authors carried out ranging and labeling the photo/video data sets as well as they selected an optimal architecture and hyperparameters for the neural network. The image test sets were used for testing the neural network trained. The findings suggest th

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50

Scott, Gary M., and W. Harmon Ray. "Neural Network Process Models Based on Linear Model Structures." Neural Computation 6, no. 4 (1994): 718–38. http://dx.doi.org/10.1162/neco.1994.6.4.718.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. This idea is extended by presenting the MANNIDENT (Multivariable Artificial Neural Network Identification) algorithm by which the mathematical equations of linear dynamic process models determine the topology and initial weights of a network, which is further trained using backpropagation. This method is applied to the task of modeling a nonisothermal chemical reactor in which a first-order exothermic reaction is occurring. This

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