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Books on the topic 'Graph-based neural network model'

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Author: Grafiati

Published: 6 September 2023

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1

T, Bialasiewicz Jan, and Langley Research Center, eds. Neural network modeling of nonlinear systems based on Volterra series extension of a linear model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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2

Neural networks and intellect: Using model-based concepts. Oxford: Oxford University Press, 2001.

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3

Grzeszczuk, Radek. Neuroanimator: Fast neural network emulation and control of physics-based models. Toronto: University of Toronto, Dept. of Computer Science, 1998.

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4

El-Gindy, Moustafa. Development of a tire/pavement contact-stress model based on artificial neural networks. McLean, Va. (6300 Georgetown Pike, McLean, 22101-2296): U.S, Dept. of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2001.

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5

El-Gindy, Moustafa. Development of a tire/pavement contact-stress model based on artificial neural networks. McLean, VA: U.S. Department of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2001.

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6

Hiroaki, Wagatsuma, ed. Neuromorphic and brain-based robots. Cambridge, UK: Cambridge University Press, 2011.

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7

Milford, Michael John. Robot navigation from nature: Simultaneous localisation, mapping, and path planning based on hippocampal models. Berlin: Springer, 2008.

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8

Shishkin, Aleksey. Methods of digital processing and speech recognition. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1904325.

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The monograph discusses the theory, algorithms and practical methods of implementing digital processing and recognition of speech signals. The basics of mathematical analysis of digital signals necessary for speech processing are presented. The acoustic theory of speech formation with the construction of a general discrete model is briefly described. The main characteristic features of speech signals, as well as methods of their isolation are considered. Hidden Markov models and the architecture of traditional recognition systems based on them are described in detail. Weighted finite converters used to increase the efficiency and speed up the process of decoding acoustic signals are considered. The main architectures of artificial neural networks and examples of integrated (end-to-end) speech recognition systems based on them are presented. It is intended for students, postgraduates, researchers and specialists dealing with speech signal processing, pattern recognition and artificial intelligence.

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9

Perlovsky, Leonid I. Neural Networks and Intellect: Using Model-Based Concepts. Oxford University Press, USA, 2000.

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10

Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.

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What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learning, defeated the world’s best Go player. The climax of this chapter is a fictional example starring Sherlock Holmes demonstrating that complex associative computation in practice has less in common with accurate pattern recognition and more with abstract high-level conceptual inference.

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11

Krichmar, Jeffrey L., and Hiroaki Wagatsuma. Neuromorphic and Brain-Based Robots. Cambridge University Press, 2011.

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12

Krichmar, Jeffrey L., and Hiroaki Wagatsuma. Neuromorphic and Brain-Based Robots. Cambridge University Press, 2011.

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13

Krichmar, Jeffrey L., and Hiroaki Wagatsuma. Neuromorphic and Brain-Based Robots. Cambridge University Press, 2011.

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14

Krichmar, Jeffrey L., and Hiroaki Wagatsuma. Neuromorphic and Brain-Based Robots. Cambridge University Press, 2020.

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15

Krichmar, Jeffrey L., and Hiroaki Wagatsuma. Neuromorphic and Brain-Based Robots. Cambridge University Press, 2012.

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16

Milford, Michael John. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer London, Limited, 2007.

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17

Milford, Michael John. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer, 2010.

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18

Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.

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This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.

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19

Artificial neural network-based methodologies for rational assessment of remaining life of existing pavements. El Paso, TX: Center for Highway Materials Research, University of Texas at El Paso, 1999.

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20

Fallani, Fabrizio, and Fabio Babiloni. Graph Theoretical Approach in Brain Functional Networks: Theory and Applications. Morgan & Claypool Publishers, 2010.

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21

Fallani, Fabrizio, and Fabio Babiloni. Graph Theoretical Approach in Brain Functional Networks: Theory and Applications. Springer International Publishing AG, 2010.

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22

Babiloni, Fabio, Fabrizio De Vico Fallani, and Fabrizio De Vico Fallani. The Graph Theoretical Approach in Brain Functional Networks: Theory and Applications. Morgan & Claypool Publishers, 2010.

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23

Statistical And Evolutionary Analysis Of Biological Networks. Imperial College Press, 2010.

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24

(Editor), Peter Lucas, José A. Gámez (Editor), and Antonio Salmerón (Editor), eds. Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing). Springer, 2007.

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25

Panzironi, Francesca. Networks. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.270.

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A network may refer to “a group of interdependent actors and the relationships among them,” or to a set of nodes linked by a web of interdependencies. The concept of networks has its origins in earlier philosophical and sociological ideas such as Jean-Jacques Rousseau’s “general will” and Émile Durkheim’s “social facts”, which adressed social and political communities and how decisions are mediated and ideas are structured within them. Networks encompass a wide range of theoretical interpretations and critical applications across different disciplines, including governance networks, policy networks, public administration networks, social movement networks, intergovernmental networks, social networks, trade networks, computer networks, information networks, and neural networks. Governance networks have been proposed as alternative pluricentric governance models representing a new form of negotiated governance based on interdependence, negotiation and trust. Such networks differ from the competitive market regulation and state hierarchical control in three aspects: the relationship between the actors, decision-making processes, and compliance. The decision-making processes within governance networks are founded on a reflexive rationality rather than the “procedural rationality” which characterizes the competitive market regulation and the “substantial rationality” which underpins authoritative state regulation. Network theory has proved especially useful for scholars in positing the existence of loosely defined and informal webs of experts or advocates that can have a real and substantial influence on international relations discourse and policy. Two examples of the use of network theory in action are transnational advocacy networks and epistemic communities.

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26

Gabora, Liane. The Creative Process of Cultural Evolution. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190455675.003.0002.

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This chapter explores how we can better understand culture by understanding the creative processes that fuel it, and better understand creativity by examining it from its cultural context. First, it summarizes attempts to develop a scientific framework for how culture evolves, and it explores what these frameworks imply for the role of creativity in cultural evolution. Next it examines how questions about the relationship between creativity and cultural evolution have been addressed using an agent-based model in which neural network-based agents collectively generate increasingly fit ideas by building on previous ideas and imitating neighbors’ ideas. Finally, it outlines studies of how creative outputs are influenced, in perhaps unexpected ways, by other ideas and individuals, and how individual creative styles “peek through” cultural outputs in different domains.

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27

Northoff, Georg. How Does the Brain’s Spontaneous Activity Generate Our Thoughts? Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.9.

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Recent investigations have demonstrated the psychological features (e.g. cognitive, affective, and social) of task-unrelated thoughts, as well as their underlying neural correlates in spontaneous activity, which cover various networks and regions, including the default-mode and central executive networks. Despite impressive progress in recent research, the mechanisms by means of which the brain’s spontaneous activity generates and constitutes thoughts remain unclear. This chapter suggests that the spatiotemporal structure of the brain’s spontaneous activity can integrate both content- and process-based approaches to task-unrelated or spontaneous thought—this amounts to what is described as the “spatiotemporal theory of task-unrelated thought” (STTT). Based on various lines of empirical evidence, the STTT postulates two main spatiotemporal mechanisms, spatiotemporal integration and extension. The STTT provides a novel brain-based spatiotemporal theory of task-unrelated thought that focuses on the brain’s spontaneous activity, including its spatiotemporal structure, which allows integrating content- and process-based approaches.

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28

Butyrskiy, Evgeniy, and Alexandr Matveev. Mathematical modeling of systems and processes. Strategy of the Future, 2022. http://dx.doi.org/10.37468/book_011222.

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The monograph considers the fundamentals of systems theory and mathematical modeling using the principles of the systems approach. In the monograph, much attention is paid to set-theoretic, dynamic, as well as aggregative and combined models. Based on the group-theoretical approach, a generalization of the theory of signals, their characteristics are considered, their classification and some theorems are carried out. A separate chapter is devoted to mathematical models of signal interaction with the propagation medium. The monograph also considers mathematical models of stochastic signal processing and control systems based on spline filtering, artificial intelligence models and neural networks. The monograph can be useful for a wide range of specialists in various fields of knowledge dealing with mathematical modeling in their research, and can also be used as a teaching aid for conducting both classroom and independent theoretical and practical classes with bachelors, masters, graduate students in the discipline "System Theory and System Analysis", "Mathematical Modeling" and "Optimal and Suboptimal Estimation of Random Processes and Systems".

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29

Roggeman, Chantal, Wim Fias, and Tom Verguts. Basic Number Representation and Beyond. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.68.

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We discuss recent computational network models of elementary number processing. One key issue to emerge from this work is a crucial distinction between symbolic and non-symbolic number representation, and the related distinction between number-selective and number-sensitive coding. Empirical predictions from the models were tested, and are here summarized. Another issue is the relation with task-based decision making mechanisms. In both lab and real-life settings, representations are seldomly accessed in a task-neutral manner, rather subjects are usually presented with a task. A related theme is the functional association between number representations and working memory. In these issues also, both modeling and neuroimaging work is summarized. To conclude, we propose that the combined modeling-neuroimaging approach should be extended to tackle more complex questions about number processing (e.g. fractions, development, dyscalculia).

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30

Churchland, Patricia S., and Terrence J. Sejnowski. The Computational Brain. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262533393.001.0001.

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Before this book was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. This book developed a different conceptual framework, based on large populations of neurons. This was done by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, the book is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers in the field of neuroscience. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of this book is still relevant.

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31

Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of random graph generation. This book is aimed at the graduate student or advanced undergraduate. It includes many worked examples, numerical simulations and exercises making it suitable for use in teaching. Explicit pseudocode algorithms are included to make the ideas easy to apply. Datasets are becoming increasingly large and network applications wider and more sophisticated. Testing hypotheses against properly specified control cases (null models) is at the heart of the ‘scientific method’. Knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.

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