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Books on the topic 'Neural fields'

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Published: 4 June 2021

Last updated: 8 June 2024

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1

Coombes, Stephen, Peter beim Graben, Roland Potthast, and James Wright, eds. Neural Fields. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1.

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2

1919-, Pribram Karl H., and Eccles, John C. Sir, 1903-, eds. Rethinking neural networks: Quantum fields and biological data. Hillsdale, N.J: Erlbaum, 1993.

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3

B, Pinter Robert, and Nabet Bahram, eds. Nonlinear vision: Determination of neural receptive fields, function, and networks. Boca Raton: CRC Press, 1992.

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4

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24406-8.

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5

Horowitz, John. The effects of hypergravic fields on neural signalling in the hippocampus. [Washington, DC: National Aeronautics and Space Administration, 1991.

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6

Booth, John Nicholas. The application of weak complex magnetic fields on the neural correlates of consciousness. Sudbury, Ont: Laurentian University, School of Graduate Studies, 2006.

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7

1919-, Pribram Karl H., and Eccles, John C. Sir, 1903-, eds. Rethinking neural networks: Quantum fields and biological data : proceedings of the First Appalachian Conference on Behavioral Neurodynamics. Hillsdale, N.J: Erlbaum, 1993.

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8

Center, Ames Research, ed. Cascading a systolic array and a feedforward neural network for navigation and obstacle avoidance using potential fields. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1991.

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9

R, Dougherty Edward, and Society of Photo-optical Instrumentation Engineers., eds. Neural, morphological, and stochastic methods in image and signal processing: 10-11 July, 1995, San Diego, California. Bellingham, Wash., USA: SPIE, 1995.

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10

Helias, Moritz, and David Dahmen. Statistical Field Theory for Neural Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46444-8.

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11

1965-, Kolen John F., and Kremer Stefan C. 1968-, eds. A field guide to dynamical recurrent networks. New York: IEEE Press, 2001.

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12

Giese, Martin A. Dynamic Neural Field Theory for Motion Perception. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5581-0.

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13

Giese, Martin A. Dynamic neural field theory for motion perception. Boston: Kluwer Academic Publishers, 1999.

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14

Giese, Martin A. Dynamic Neural Field Theory for Motion Perception. Boston, MA: Springer US, 1999.

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15

MacLennan, Bruce J. Field computation and nonpropositional knowledge. Monterey, California: Naval Postgraduate School, 1987.

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16

1994, Altherr Tanguy d., Aurenche P, Veneziano G, and Sorba P, eds. From thermal field theory to neural networks: A day to remember Tanguy Altherr, Cern, 4 November 1994. Singapore: World Scientific, 1996.

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17

Heida, Tjitske. Electric Field-Induced Effects on Neuronal Cell Biology Accompanying Dielectrophoretic Trapping. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55469-8.

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18

Potthast, Roland, P. Beim Graben, Wright James, and Stephen Coombes. Neural Fields: Theory and Applications. Springer Berlin / Heidelberg, 2016.

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19

Potthast, Roland, Wright James, Stephen Coombes, and Peter beim Graben. Neural Fields: Theory and Applications. Springer London, Limited, 2014.

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20

Neural Fields: Theory and Applications. Springer, 2014.

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21

Bressloff, Paul C. Waves in Neural Media: From Single Neurons to Neural Fields. Springer London, Limited, 2013.

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22

Bressloff, Paul C. Waves in Neural Media: From Single Neurons to Neural Fields. Springer, 2013.

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23

Waves In Neural Media From Single Neurons To Neural Fields. Springer-Verlag New York Inc., 2013.

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24

Pribram, Karl H. Rethinking Neural Networks: Quantum Fields and Biological Data. Taylor & Francis Group, 2016.

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25

Pribram, Karl H. Rethinking Neural Networks: Quantum Fields and Biological Data. Taylor & Francis Group, 2014.

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26

Pribram, Karl H. Rethinking Neural Networks: Quantum Fields and Biological Data. Taylor & Francis Group, 2014.

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27

Pribram, Karl H. Rethinking Neural Networks: Quantum Fields and Biological Data. Taylor & Francis Group, 2014.

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28

Pribram, Karl H. Rethinking Neural Networks: Quantum Fields and Biological Data. Taylor & Francis Group, 2014.

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29

Pribram, Karl H. Rethinking Neural Networks: Quantum Fields and Biological Data. Lawrence Erlbaum, 1993.

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30

El-Hayek, Youssef. Regulation of neural migration by direct-current electric fields. 2006.

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31

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer London, Limited, 2015.

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32

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer, 2015.

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33

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer International Publishing AG, 2016.

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34

Pinter, Robert B. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Taylor & Francis Group, 2018.

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35

Pinter, Robert B. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Edited by Robert B. Pinter and Bahram Nabet. CRC Press, 2018. http://dx.doi.org/10.1201/9781351075060.

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36

Nonlinear Vision: Determination of Neural Receptive Fields Function and Networks. Taylor & Francis Group, 2017.

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37

Pinter, Robert B. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Taylor & Francis Group, 2018.

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38

Pinotsis, Dimitris, Peter Robinson, Peter beim Graben, and Karl Friston, eds. Neural Masses and Fields: Modelling the Dynamics of Brain Activity. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-427-8.

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39

Pinter, Robert B. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Crc Pr I Llc, 1992.

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40

Pinter, Robert B. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Taylor & Francis Group, 2018.

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41

National Aeronautics and Space Administration (NASA) Staff. Effects of Hypergravic Fields on Neural Signalling in the Hippocampus. Independently Published, 2018.

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42

Pinter, Robert B. Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks. Taylor & Francis Group, 2018.

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43

Sharpe, Christina Elizabeth. Martine Syms : Neural Swamp: The Future Fields Commission in Time-Based Media. Yale University Press, 2022.

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44

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

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45

Effect of 60 Hz electric and magnetic fields on neural and skeletal cells in culture: Contractor's final report. Albany, N.Y: New York State Power Lines Project, 1987.

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46

National Aeronautics and Space Administration (NASA) Staff. Cascading a Systolic Array and a Feedforward Neural Network for Navigation and Obstacle Avoidance Using Potential Fields. Independently Published, 2018.

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47

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

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48

Brain dynamics: Progress and perspectives. Berlin: Springer-Verlag, 1989.

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49

Basar, Erol. Brain Dynamics: Progress and Perspectives : Based on a Conference in West Berlin in August 1987 (Springer Studies in Brain Dynamics, Vol 2). Springer-Verlag, 1989.

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50

Melnechuk, Theodore, and Erol Baºar. Dynamics of Sensory and Cognitive Processing by the Brain: Integrative Aspects of Neural Networks, Electroencephalography, Event-Related Potentials, Contingent Negative Variation, Magnetoencephalography, and Clinical Applications. Springer London, Limited, 2011.

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