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Relevant bibliographies by topics / Models of neural elements

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Academic literature on the topic 'Models of neural elements'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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Journal articles on the topic "Models of neural elements"

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NAMAZOV, S. A., R. M. KHUSAINOV, and N. G. TALIPOV. "MODELS FOR RECOGNITION OF ROAD INFRASTRUCTURE ELEMENTS." Herald of Technological University 27, no. 3 (2024): 90–95. http://dx.doi.org/10.55421/1998-7072_2024_27_3_90.

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The paper considers the problem of recognising road infrastructure elements (temporary signs, guardrail tapes, traffic cones). To solve it, we propose to use the following types of models: Deep Learning model, Random Forest model, Support Vector Machines model, Directional Gradient Histogram model. The principles of operation of the models presented in the article are considered. Recognizing road infrastructure elements in three of the four models studied requires a significant amount of time. The factors influencing the implementation of road works and object recognition are analyzed. The nee

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Teslyuk, V. M., and S. S. Ivasiv. "Models and means of clothing elements patterns classification using machine learning." Ukrainian Journal of Information Technology 6, no. 1 (2024): 37–47. http://dx.doi.org/10.23939/ujit2024.01.037.

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The task of pattern classification remains relevant in the fields of trends, style, fashion, personalization, manufacturing, and design. Research aimed at the design and development of models and means of classification of patterns of clothing elements using machine learning is highlighted. The study addresses a pertinent issue in computer vision, namely: increasing the efficiency of classification of patterns of clothing elements. The research was conducted with a proprietary dataset comprising 600 images. The following patterns are defined for classification: “checkered”, “dotted”, “vegetati

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І.Г., Цмоць, Лукащук Ю.А., Хавалко В.М. та Рабик В.Г. "Моделі нейроподібного елемента паралельно-паралельного типу". Моделювання та інформаційні технології, № 86 (15 листопада 2019): 119–26. https://doi.org/10.5281/zenodo.3610679.

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Models of neural elements of parallel-parallel type with direct and tabular formation of macroparticle products have been developed, the main components of such neural elements have been identified, and it has been proposed to adapt the neural elements to the requirements by splitting into conveyor steps of appropriate complexity.

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Bainbridge, William Sims. "Neural Network Models of Religious Belief." Sociological Perspectives 38, no. 4 (1995): 483–95. http://dx.doi.org/10.2307/1389269.

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This paper applies neural network technology, a standard approach in computer science that has been unaccountably ignored by sociologists, to the problem of developing rigorous sociological theories. A simulation program employing a “varimax” model of human learning and decision-making models central elements of the Stark-Bainbridge theory of religion. Individuals in a micro-society of 24 simulated people learn which categories of potential exchange partners to seek for each of four material rewards which in fact can be provided by other actors in the society. However, when they seek eternal l

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Yakov, Vladimir, and Petar Hristov. "Predicting the parameters of reinforced concrete elements by neural network." IOP Conference Series: Materials Science and Engineering 1323, no. 1 (2024): 012019. https://doi.org/10.1088/1757-899x/1323/1/012019.

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Abstract Obtaining predictive results at planning and conceptual design phase often represents a serious problem on which the choice of design options depends to a significant extent This paper seeks solutions to the problem using neural networks and artificial intelligence and illustrates the idea by simple examples of pre-trained neural network models.

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Yaish, Ofir, and Yaron Orenstein. "Computational modeling of mRNA degradation dynamics using deep neural networks." Bioinformatics 38, no. 4 (2021): 1087–101. http://dx.doi.org/10.1093/bioinformatics/btab800.

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Abstract Motivation messenger RNA (mRNA) degradation plays critical roles in post-transcriptional gene regulation. A major component of mRNA degradation is determined by 3′-UTR elements. Hence, researchers are interested in studying mRNA dynamics as a function of 3′-UTR elements. A recent study measured the mRNA degradation dynamics of tens of thousands of 3′-UTR sequences using a massively parallel reporter assay. However, the computational approach used to model mRNA degradation was based on a simplifying assumption of a linear degradation rate. Consequently, the underlying mechanism of 3′-U

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Govindwar, Vaibhav, Aman Akbani, Aachal Nandeshwar, Aishwarya Wanjari, Prachi Agashe, and Charan Pote. "Facial Expression Recognition Using Convolutional Neural Network." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 786–94. http://dx.doi.org/10.32628/cseit24102122.

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Understanding others' intentions through nonverbal cues like facial emotions is crucial in human communication. To design and train Deep Learning Models, this paper describes in detail how Convolutional Neural Network Models are developed using tf. Keras. The aim is to Sort facial photos into one of the seven face detection classifiers, our model is developed in such a manner that it learns hidden nonlinearity from the entered facial images, which is vital for discriminating the form of emotion someone is expressing. The model proposed on the Lenet-5 architecture by Yann LeCun uses the subsamp

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Wang, Zhaojun, Jiangning Wang, Congtian Lin, Yan Han, Zhaosheng Wang, and Liqiang Ji. "Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks." Animals 11, no. 5 (2021): 1263. http://dx.doi.org/10.3390/ani11051263.

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With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird

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Seeliger, K., L. Ambrogioni, Y. Güçlütürk, L. M. van den Bulk, U. Güçlü, and M. A. J. van Gerven. "End-to-end neural system identification with neural information flow." PLOS Computational Biology 17, no. 2 (2021): e1008558. http://dx.doi.org/10.1371/journal.pcbi.1008558.

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Neural information flow (NIF) provides a novel approach for system identification in neuroscience. It models the neural computations in multiple brain regions and can be trained end-to-end via stochastic gradient descent from noninvasive data. NIF models represent neural information processing via a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatiotemporal location. Each tensor is coupled to the m

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Pinjarkar, Latika, Aditya Nittala, Mahantesh P. Mattada, Vedant Pinjarkar, Bhumika Neole, and Manisha Kogundi Math. "Optimizing neural radiance field: a comprehensive review of the impact of different optimizers on neural radiance fields." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 476–84. http://dx.doi.org/10.11591/eei.v14i1.8315.

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Neural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the mod

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Dissertations / Theses on the topic "Models of neural elements"

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Andrzej, Tuchołka. "Methodology for assessing the construction of machine elements using neural models and antipatterns : doctoral dissertation." Rozprawa doktorska, [s.n.], 2020. http://dlibra.tu.koszalin.pl/Content/1317.

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Miocinovic, Svjetlana. "THEORETICAL AND EXPERIMENTAL PREDICTIONS OF NEURAL ELEMENTS ACTIVATED BY DEEP BRAIN STIMULATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1181758206.

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Tomov, Petar Georgiev. "Interplay of dynamics and network topology in systems of excitable elements." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17464.

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Wir untersuchen globale dynamische Phänomene, die sich von dem Zusammenspiel zwischen Netzwerktopologie und Dynamik der einzelnen Elementen ergeben. Im ersten Teil untersuchen wir relativ kleine strukturierte Netzwerke mit überschaubarer Komplexität. Als geeigneter theoretischer Rahmen für erregbare Systeme verwenden wir das Kuramoto und Shinomoto Modell der sinusförmig-gekoppelten "aktiven Rotatoren" und studieren das Kollektivverhalten des Systems in Bezug auf Synchronisation. Wir besprechen die Einschränkungen, die durch die Netzwerktopologie auf dem Fluss im Phasenraum des Systems geste

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Wadagbalkar, Pushkar. "Real-time prediction of projectile penetration to laminates by training machine learning models with finite element solver as the trainer." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592169428128864.

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Citipitioglu, Ahmet Muhtar. "Development and assessment of response and strength models for bolted steel connections using refined nonlinear 3D finite element analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31691.

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Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Haj-Ali, Rami; Committee Co-Chair: Leon, Roberto; Committee Co-Chair: White, Donald; Committee Member: DesRoches, Reginald; Committee Member: Gentry, Russell. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Паржин, Юрій Володимирович. "Моделі і методи побудови архітектури і компонентів детекторних нейроморфних комп'ютерних систем". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/34755.

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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.05 – комп'ютерні системи та компоненти. – Національний технічний університет "Харківський політехнічний інститут", Міністерство освіти і науки України, Харків, 2018. Дисертація присвячена вирішенню проблеми підвищення ефективності побудови та використання нейроморфних комп'ютерних систем (НКС) в результаті розробки моделей побудови їх компонентів та загальної архітектури, а також методів їх навчання на основі формалізованого детекторного принципу. В результаті аналізу і класифікації архітектури та компонент

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Паржин, Юрій Володимирович. "Моделі і методи побудови архітектури і компонентів детекторних нейроморфних комп'ютерних систем". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/34756.

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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.05 – комп'ютерні системи та компоненти. – Національний технічний університет "Харківський політехнічний інститут", Міністерство освіти і науки України, Харків, 2018. Дисертація присвячена вирішенню проблеми підвищення ефективності побудови та використання нейроморфних комп'ютерних систем (НКС) в результаті розробки моделей побудови їх компонентів та загальної архітектури, а також методів їх навчання на основі формалізованого детекторного принципу. В результаті аналізу і класифікації архітектури та компонен

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Levin, Robert Ian. "Dynamic Finite Element model updating using neural networks." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264075.

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Stevenson, King Douglas Beverley. "Robust hardware elements for weightless artificial neural networks." Thesis, University of Central Lancashire, 2000. http://clok.uclan.ac.uk/1884/.

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This thesis investigates novel robust hardware elements for weightless artificial neural systems with a bias towards high integrity avionics applications. The author initially reviews the building blocks of physiological neural systems and then chronologically describes the development of weightless artificial neural systems. Several new design methodologies for the implementation of robust binary sum-and-threshold neurons are presented. The new techniques do not rely on weighted binary counters or registered arithmetic units for their operation making them less susceptible to transient single

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Venkov, Nikola A. "Dynamics of neural field models." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517742.

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Books on the topic "Models of neural elements"

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De Wilde, Philippe. Neural Networks Models. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0034478.

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De Wilde, Philippe. Neural Network Models. Springer London, 1997. http://dx.doi.org/10.1007/978-1-84628-614-8.

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Domany, Eytan, J. Leo van Hemmen, and Klaus Schulten, eds. Models of Neural Networks. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97171-6.

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Domany, Eytan, J. Leo van Hemmen, and Klaus Schulten, eds. Models of Neural Networks. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4320-5.

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Domany, Eytan. Models of Neural Networks. Springer Berlin Heidelberg, 1991.

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1947-, Domany E., Hemmen, J. L. van 1947-, Schulten K, and Braitenberg Valentino, eds. Models in neural networks. Springer-Verlag, 1991.

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K, Mohan Chilukuri, and Ranka Sanjay, eds. Elements of artificial neural networks. MIT Press, 1997.

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Domany, Eytan, J. Leo van Hemmen, and Klaus Schulten, eds. Models of Neural Networks III. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0723-8.

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van Hemmen, J. Leo, Jack D. Cowan, and Eytan Domany, eds. Models of Neural Networks IV. Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21703-1.

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Domany, Eytan, J. Leo van Hemmen, and Klaus Schulten, eds. Models of Neural Networks I. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79814-6.

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Book chapters on the topic "Models of neural elements"

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Troiano, Amedeo, Fernando Corinto, and Eros Pasero. "A Memristor Circuit Using Basic Elements with Memory Capability." In Recent Advances of Neural Network Models and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04129-2_12.

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Arle, Jeffrey E., Longzhi Mei, and Kristen W. Carlson. "Robustness in Neural Circuits." In Brain and Human Body Modeling 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_12.

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AbstractComplex systems are found everywhere – from scheduling to traffic, food to climate, economics to ecology, the brain, and the universe. Complex systems typically have many elements, many modes of interconnectedness of those elements, and often exhibit sensitivity to initial conditions. Complex systems by their nature are generally unpredictable and can be highly unstable.

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Del Moral Hernandez, Emilio. "Studying Neural Networks of Bifurcating Recursive Processing Elements — Quantitative Methods for Architecture Design." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_65.

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Kashchenko, Serguey. "Model of the Neural System with Diffusive Interaction of Elements." In Lecture Notes in Morphogenesis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19866-8_7.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_12.

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AbstractIn this chapter, we provide the main elements for implementing deep neural networks in Keras for binary, categorical, and mixed outcomes under feedforward networks as well as the main practical issues involved in implementing deep learning models with binary response variables. The same practical issues are provided for implementing deep neural networks with categorical and count traits under a univariate framework. We follow with a detailed assessment of information for implementing multivariate deep learning models for continuous, binary, categorical, count, and mixed outcomes. In al

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Sommer, D., B. Troff, and P. Middendorf. "Artificial neural networks as material models for finite element analysis." In Current Perspectives and New Directions in Mechanics, Modelling and Design of Structural Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9781003348443-43.

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Sommer, D., B. Troff, and P. Middendorf. "Artificial neural networks as material models for finite element analysis." In Current Perspectives and New Directions in Mechanics, Modelling and Design of Structural Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9781003348450-43.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_11.

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AbstractThis chapter provides elements for implementing deep neural networks (deep learning) for continuous outcomes. We give details of the hyperparameters to be tuned in deep neural networks and provide a general guide for doing this task with more probability of success. Then we explain the most popular deep learning frameworks that can be used to implement these models as well as the most popular optimizers available in many software programs for deep learning. Several practical examples with plant breeding data for implementing deep neural networks in the Keras library are outlined. These

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Riel, Stefanie, Mohammad Bashiri, Werner Hemmert, and Siwei Bai. "Computational Models of Brain Stimulation with Tractography Analysis." In Brain and Human Body Modeling 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_6.

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AbstractComputational human head models have been used in studies of brain stimulation. These models have been able to provide useful information that can’t be acquired or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. We hereby combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogr

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Soloviev, Arcady, Boris Sobol, Pavel Vasiliev, and Alexander Senichev. "Generative Artificial Neural Network Model for Visualization of Internal Defects of Structural Elements." In Springer Proceedings in Materials. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45120-2_48.

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Conference papers on the topic "Models of neural elements"

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Karilanova, Sanja, Subhrakanti Dey, and Ayça Özçelikkale. "State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neurons." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065909.

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Meyer, Svea Marie, Philipp Weidel, Philipp Plank, et al. "A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065663.

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Leto, Benedetto, Gianvito Urgese, Enrico Macii, and Vittorio Fra. "A LIF-based Legendre Memory Unit as neuromorphic State Space Model benchmarked on a second-long spatio-temporal task." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065250.

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Koenig, Aymeric, Benjamin Allaert, and Emmanuel Renaux. "NEURAL-UML: Intelligent Recognition System of Structural Elements in UML Class Diagram." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00099.

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Kaminski, Marcin, and Mateusz Malarczyk. "Neural Data Processing in Scanner of Static Elements." In 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2019. http://dx.doi.org/10.1109/mmar.2019.8864650.

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Wang, Felix, Corinne Teeter, Sarah Luca, Srideep Musuvathy, and Brad Aimone. "Localization through Grid-basedEncodings on Digital Elevation Models." In NICE 2022: Neuro-Inspired Computational Elements Conference. ACM, 2022. http://dx.doi.org/10.1145/3517343.3517366.

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Leugering, Johannes. "Making spiking neurons more succinct with multi-compartment models." In NICE '20: Neuro-inspired Computational Elements Workshop. ACM, 2020. http://dx.doi.org/10.1145/3381755.3381763.

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Maloney, Laurence T. "Neural and neurally inspired network models for vision." In OSA Annual Meeting. Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.thf1.

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This tutorial provides an understanding of current and past attempts to model vision using simple computing elements arranged as a network. The models examined include the highly publicized parallel distributed processing models described by Rumelhart and McClelland, the work of Stephen Grossberg, and others. The primary goal is to taxonomize, explain, and evaluate these models, measuring their capabilities against long-standing problems in vision, visual development, and computer vision.

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Allen, Kathleen B., and Bradley E. Layton. "Mechanical Neural Growth Models." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79445.

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Critical to being able to control the growth patterns of cell-based sensors is being able to understand how the cytoskeleton of the cell maintains its structure and integrity both under mechanical load and in a load-free environment. Our approach to a better understanding of cell growth is to use a computer simulation that incorporates the primary structures, microtubules, necessary for growth along with their observed behaviors and experimentally determined mechanical properties. Microtubules are the main compressive structural support elements for the axon of a neuron and are created via pol

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Morozov, Aleksandr, Karine Abgaryan, and Dmitry Reviznikov. "TWO-STEP METHOD FOR CONSTRUCTING COMPACT MODELS OF RERAM ELEMENTS." In Mathematical modeling in materials science of electronic component. LCC MAKS Press, 2024. http://dx.doi.org/10.29003/m4281.mmmsec-2024/137-141.

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Mathematical models of memristors are usually specified as dynamic systems relative to the state parameter of the element characterizing conductivity. In this case, the dependence of the current (or resistance) on the state parameter and the voltage applied to the element can be quite complex. Existing models often do not fully reproduce experimental data. In this regard, the paper proposes a two-stage method consisting of using classical models at the first stage and neural networks to approximate the dependence of the current on the state parameter and voltage at the second stage.

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Reports on the topic "Models of neural elements"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa

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Warrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7695865.bard.

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The project exploits the use of Artificial Neural Networks (ANN) to describe infiltration, water, and solute distribution in the soil during irrigation. It provides a method of simulating water and solute movement in the subsurface which, in principle, is different and has some advantages over the more common approach of numerical modeling of flow and transport equations. The five objectives were (i) Numerically develop a database for the prediction of water and solute distribution for irrigation; (ii) Develop predictive models using ANN; (iii) Develop an experimental (laboratory) database of

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Brown, Joshua W. Computational Neural Models of Risk. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada515423.

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Byrne, John H. Analysis and Synthesis of Adaptive Neural Elements. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada187047.

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Gardner, Daniel. Symbolic Processor Based Models of Neural Networks. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200200.

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Casey, Tiernan, and Bert Debusschere. Analysis of Neural Network Combustion Surrogate Models. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1569154.

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Byrne, John H. Analysis and Synthesis of Adaptive Neural Elements and Assemblies. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada201239.

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Leij, F. J., and M. T. Van Genuchten. Development of Pedotransfer Functions with Neural Network Models. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada394563.

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Fan, Hongyou, Catherine Branda, Richard Louis Schiek, Christina E. Warrender, and James Chris Forsythe. Neural assembly models derived through nano-scale measurements. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/993899.

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Niederer, J. Particle Beam Control Design Notes for Neural Models. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/1151384.

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