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Relevant bibliographies by topics / Neural structures

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Academic literature on the topic 'Neural structures'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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Journal articles on the topic "Neural structures"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Neural structures"

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Kirikera, Goutham Raghavendra. "A Structural Neural System for Health Monitoring of Structures." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1155149869.

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Cohen, Michael A. "High-level neural structures constrain visual behavior." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11447.

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Visual cognition is notoriously limited: only a finite amount of information can be fully processed at a given instant. What is the source of these limitations? Here, we suggest that the organization of higher-level visual cortex into content-specific channels constrains information processing across the visual system. Each channel is primarily involved in representing one particular type of visual content (e.g. faces, cars, certain types of shapes, etc.). Furthermore, each channel has a finite processing capacity/bandwidth and is limited in the amount of information it can process. When mult

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Coelho, Regina Célia. "Síntese,modelagem e simulação de estruturas neurais morfologicamente realísticas." Universidade de São Paulo, 1998. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-15052009-091429/.

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Os aspectos morfológicos dos neurônios e estruturas neurais, embora potencialmente importantes, têm recebido relativamente pouca atenção na literatura em neurociência. Este trabalho consiste numa substancial parte de um projeto em desenvolvimento no Grupo de Pesquisa em Visão Cibernética voltado para o estudo da relação formal/função neural. Mais especificamente, o presente trabalho dedica particular atenção para a síntese, modelagem e simulação de estruturas neurais morfologicamente realísticas. A tese se inicia com revisões bibliográficas sobre visão biológica e neurociência, direcionadas ao

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Neville, Richard. "Augmentation of sigma-pi structures and learning regimes." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359920.

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Elhewy, Ahmed. "Probabilistic analysis of composite structures using artificial neural network." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413045.

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Oyallon, Edouard. "Analyzing and introducing structures in deep convolutional neural networks." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE060.

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Cette thèse étudie des propriétés empiriques des réseaux de neurones convolutifs profonds, et en particulier de la transformée en Scattering. En effet, l’analyse théorique de ces derniers est difficile et représente jusqu’à ce jour un défi : les couches successives de neurones ont la capacité de réaliser des opérations complexes, dont la nature est encore inconnue, via des algorithmes d’apprentissages dont les garanties de convergences ne sont pas bien comprises. Pourtant, ces réseaux de neurones sont de formidables outils pour s’attaquer à une grande variété de tâches difficiles telles la cla

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Search, David John. "Inspection of periodic structures using coherent optics." Thesis, Liverpool John Moores University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242378.

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Cheng, Yi-Hsun Ethan. "Memory-based learning structure : learning covergence [sic], network structures and training techniques /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974613.

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Gil, Ferrer Alejandro. "A neural network performance analysis with three different model structures." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302144.

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This report analyzes three neural network structures: dense, convolutional and recurrent. One data set example and problem has been chosen for each type of structure: a multi-class classification problem, an image classifier and a time sequence prediction, respectively. This report also aims at understanding which structure performs better for different problem statements, and how the different parameters they depend on affect their performance. The most common parameters that have been analyzed are the following: the number of intermediate layers, the number of neurons, the number of epochs,

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Large, Imogen. "Investigating neural structures and behavioural biases in perceptual decision-making." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:1a107371-424b-4dfd-96e4-d851d801bec8.

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Why do we behave as we do? The general mechanisms underlying visual perception and decision-making within a social context have been under scientific scrutiny for over half a century (Sherif, 1945; Asch, 1951; Berns et al, 2005). In spite of this, neither a definitive mechanism for how perceptual biases arise, nor a robust neural basis have emerged. In the first part of my thesis, I use a combination of visual behavioural testing and computational modelling to investigate the development of perceptual biases under social advice in children, and explore their potential mechanisms. In the second

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Books on the topic "Neural structures"

1

Vasquez, Daniel, Rainer Gruhn, and Wolfgang Minker. Hierarchical Neural Network Structures for Phoneme Recognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34425-1.

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von, Seelen W., Shaw G. L. 1932-, Leinhos U. M, and Werner-Reimers-Stiftung, eds. Organization of neural networks: Structures and models. VCH, 1988.

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3

Roberts, S. G. The evolution of artificial neural network structures. UMIST, 1997.

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Vasquez, Daniel. Hierarchical Neural Network Structures for Phoneme Recognition. Springer Berlin Heidelberg, 2013.

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Rutkowski, Leszek. Flexible neuro-fuzzy systems: Structures, learning, and performance evaluation. Kluwer Academic Publishers, 2004.

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Waszczyszyn, Zenon, ed. Neural Networks in the Analysis and Design of Structures. Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-2484-0.

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Elsayed, Gamaleldin Fathy. Identification and Validation of Structures in Neural Population Responses. [publisher not identified], 2017.

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Lim, Peter Chen Yuen. Detecting and diagnosing faults in aerospace structures using neural networks. University of Manchester, 1995.

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9

Dong, Tiansi. A Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56275-5.

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Petridis, Vassilios. Predictive Modular Neural Networks: Applications to Time Series. Springer US, 1998.

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Book chapters on the topic "Neural structures"

1

Osipyan, Hasmik, Bosede Iyiade Edwards, and Adrian David Cheok. "Neural Network Structures." In Deep Neural Network Applications. CRC Press, 2022. http://dx.doi.org/10.1201/9780429265686-3.

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Markova, Gabriela, James Stieben, and Maria Legerstee. "Neural Structures of Jealousy." In Handbook of Jealousy. Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444323542.ch5.

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Cabanes, Guénaël, and Younès Bennani. "Comparing Large Datasets Structures through Unsupervised Learning." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_62.

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de Blasio, Gabriel, Arminda Moreno-Díaz, Roberto Moreno-Díaz, and Roberto Moreno-Díaz. "New Biomimetic Neural Structures for Artificial Neural Nets." In Computer Aided Systems Theory – EUROCAST 2011. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27549-4_4.

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Singh, Sameer. "Neural Learning of Spiral Structures." In International Conference on Advances in Pattern Recognition. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0833-7_23.

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Mariyama, Toshisada, Kunihiko Fukushima, and Wataru Matsumoto. "Automatic Design of Neural Network Structures Using AiS." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_32.

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Albrecht, Hans, Jan Wirnitzer, and Lothar Gaul. "Damping of Structural Vibrations Using Adaptive Joint Connections and Neural Control." In Smart Structures. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-2686-8_8.

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Kapoor, Nishant Raj, Aman Kumar, Harish Chandra Arora, and Ashok Kumar. "Structural Health Monitoring of Existing Building Structures for Creating Green Smart Cities Using Deep Learning." In Recurrent Neural Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822-15.

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Siegelmann, Hava T. "Neural dynamics with stochasticity." In Adaptive Processing of Sequences and Data Structures. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054004.

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Lugiato, L. A., W. Kaige, L. M. Narducci, et al. "Cooperative Frequency Locking and Spatial Structures in Lasers." In Neural and Synergetic Computers. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74119-7_17.

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Conference papers on the topic "Neural structures"

1

TURNER, CHARLIE. "Application of neural networks to smart structures." In 32nd Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-1235.

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Juang, Jyh-Ching, Chi-Yuan Chiang, and Hussein Youssef. "Structural modeling and control using neural networks." In 35th Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1521.

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HAAS, DAVID, JOEL MILANO, and LANCE FLITTER. "PREDICTION OF HELICOPTER COMPONENT LOADS USING NEURAL NETWORKS." In 34th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1301.

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TSOU, POYU, and M. SHEN. "STRUCTURAL DAMAGE DETECTION AND IDENTIFICATION USING NEURAL NETWORK." In 34th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1708.

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Szewczyk, Z., and Prabhat Hajela. "Function extrapolation in structural reanalysis using neural networks." In 35th Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1599.

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SWIFT, R., and S. BATILL. "Application of neural networks to preliminary structural design." In 32nd Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-1038.

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YEN, GARY, and MOON KWAK. "NEURAL NETWORK APPROACH FOR THE DEMAGE DETECTION OF STRUCTURES." In 34th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1485.

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Mohajeri, Kamran, Ghasem Pishehvar, and Mohammad Seifi. "CMAC neural networks structures." In 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA 2009). IEEE, 2009. http://dx.doi.org/10.1109/cira.2009.5423175.

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Kodiyalam, Srinivas, and Ram Gurumoorthy. "Neural networks with modified backpropagation learning - Applied to structural optimization." In 36th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-1370.

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"FATIGUE CRACK GROWTH PREDICTION FOR SPECTRUM LOADINGS USING NEURAL NETWORKS." In 34th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1609.

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Reports on the topic "Neural structures"

1

Metz, S., M. O. Heuschkel, B. V. Avila, R. Holzer, and D. Bertrand. Microelectrodes with Three-Dimensional Structures for Improved Neural Interfacing. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada412975.

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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models ar

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint

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Chang, Ke-Vin. Assessing Carpal Tunnel and Associated Neural Structures with Superb Microvascular Imaging: A Study Protocol of Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.10.0015.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yiel

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio

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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48420.

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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential

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Gonzalez Pibernat, Gabriel, and Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.

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This article examines the practical applications of single hidden layer neural networks in machine learning and artificial intelligence. They have been used in diverse fields, such as finance, medicine, and autonomous vehicles, due to their simplicit

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Perdigão, Rui A. P. Neuro-Quantum Cyber-Physical Intelligence (NQCPI). Synergistic Manifolds, 2024. http://dx.doi.org/10.46337/241024.

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Neuro-Quantum Cyber-Physical Intelligence (NQCPI) is hereby introduced, entailing a novel framework for nonlinear natural-based neural post-quantum information physics, along with novel advances in far-from-equilibrium thermodynamics and evolutionary cognition in post-quantum neurobiochemistry for next-generation information physical systems intelligence. NQCPI harnesses and operates with the higher-order nonlinear nature of previously elusive quantum behaviour, including in open chaotic dissipative systems in thermodynamically and magneto-electrodynamically disruptive conditions, such as in n

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Siegel, Frank L., and Steven E. Kornguth. Effects of JP8 on Neural Structure and Function. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387032.

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