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Books on the topic 'Neuron generation'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Konieczny, Marek A. Generation of neutron kerma factors from double-differential neutron data. University of Birmingham, 1991.

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2

Kumar, Das Basanta. Development of compact D-D neutron generator. Bhabha Atomic Research Centre, 2011.

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3

Souček, Branko. Neural and massively parallel computers: The sixth generation. Wiley, 1988.

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4

Souc̆ek, Branko. Neural and concurrent real-time systems: The sixth generation. Wiley, 1989.

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5

C, Jain L., and Johnson R. P, eds. Automatic generation of neural network architecture using evolutionary computation. World Scientific, 1997.

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6

Neural and concurrent real-time systems: The sixth generation. Wiley, 1989.

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7

Gusmão, António, Nuno Horta, Nuno Lourenço, and Ricardo Martins. Analog IC Placement Generation via Neural Networks from Unlabeled Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50061-0.

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8

Group, IRIS, ed. Neural and intelligent systems integration: Fifth and sixth generation integrated reasoning information systems. Wiley, 1991.

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9

Immer wieder Familie: Familien- und Generationenromane in der neueren Literatur. StudienVerlag, 2012.

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10

The informational complexity of learning: Perspectives on neural networks and generative grammar. Kluwer Academic Publishers, 1998.

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11

Niyogi, Partha. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar. Springer US, 1998.

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12

Ahmed, Khalid. A computer model of a neural pattern generator for the canine small intestine. National Library of Canada = Bibliothèque nationale du Canada, 1993.

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13

R, Rolland, ed. Neuro-endocrinology of reproduction: Proceedings of the VIth Reinier de Graaf Symposium, Nijmegen, the Netherlands, 27-29 August 1987. Excerpta Medica, 1987.

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14

International Forum on Applications of Neural Networks to Power Systems (2nd 1993 Yokohama-shi, Japan). ANNPS '93: Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems, April 19-22, 1993, Yokohama, Japan. IEEE, 1993.

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15

Ryabov, Vladimir. Oil and Gas Chemistry. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1017513.

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The textbook provides up-to-date data on the composition and properties of hydrocarbons and other oil and gas compounds, on the physical and chemical methods and methods for separating and identifying oil components (molecular spectroscopy, mass spectrometry, NMR spectroscopy, electron paramagnetic resonance, atomic adsorption spectroscopy, neutron activation analysis). The chemistry and mechanism of thermal and catalytic transformations of oil components in the main processes of oil raw materials processing, as well as the problems of the origin of oil and the transformation of oil in the env

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16

Thornborough, John. Neuron Function: GENERATION & CONDUCTION OF ACTION POTENTIALS, COMMUNICATN BETWEEN CELLS (PRETEST KEY CONCEPTS). Edited by John Thornborough. McGraw-Hill Book Company, 1995.

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17

Harms, Matthew B., and Timothy M. Miller. Amyotrophic Lateral Sclerosis. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0027.

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Recent advances in sequencing technologies have dramatically expanded the number of genes associated with amyotrophic lateral sclerosis, including rare but highly penetrant causative mutations as well as common risk alleles. This chapter discusses these gene discoveries and how they have implicated a diverse array of biological pathways essential for motor neuron health and have begun to inform our understanding of ALS pathogenesis as a heterogeneous and multistep process. Insights from these discoveries are leading to a new generation of targeted therapies directed at specific genes and are p

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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 generatin

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19

Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can b

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20

Nat, Roxana, and Andreas Eigentler. Cell Culture, iPS Cells and Neurodegenerative Diseases. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190233563.003.0013.

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Somatic reprogramming technology, which enables the conversion of adult human non-neural cells into neurons, has progressed rapidly in recent years. The derivation of patient-specific induced pluripotent stem (iPS) cells has become routine. The inherent broad differentiation potential of iPS cells makes possible the generation of diverse types of human neurons. This constitutes a remarkable step in facilitating the development of more appropriate and comprehensive preclinical human disease models, as well as for high throughput drug screenings and cell therapy. This chapter reviews recent prog

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21

Soucek, Branko, and The IRIS Group. Dynamic, Genetic, and Chaotic Programming: The Sixth-Generation (Sixth Generation Computer Technologies). Wiley-Interscience, 1992.

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22

Byrne, John H., ed. The Oxford Handbook of Invertebrate Neurobiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190456757.001.0001.

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Invertebrates have proven to be extremely useful models for gaining insights into the neural and molecular mechanisms of sensory processing, motor control, and higher functions, such as feeding behavior, learning and memory, navigation, and social behavior. Their enormous contribution to neuroscience is due, in part, to the relative simplicity of invertebrate nervous systems and, in part, to the large cells found in some invertebrates, like mollusks. Because of the organizms’ cell size, individual neurons can be surgically removed and assayed for expression of membrane channels, levels of seco

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23

Selverston, Allen. Rhythms and oscillations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0021.

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The study of identifiable neurons, a common feature of invertebrate nervous systems, has made it possible to construct a detailed cell-to-cell connectivity map using electrophysiological methods that can inspire the design of biomimetic systems. This chapter describes how the analysis of the neural circuitry in the lobster stomatogastric ganglion (STG) has provided some general principles underlying oscillatory and rhythmic behavior in all animals. The rhythmic and oscillatory patterns produced by the two STG central pattern generating (CPG) circuits are a result of two cooperative mechanisms,

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24

Schaible, Hans-Georg, and Rainer H. Straub. Pain neurophysiology. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0059.

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Physiological pain is evoked by intense (noxious) stimuli acting on healthy tissue functioning as a warning signal to avoid damage of the tissue. In contrast, pathophysiological pain is present in the course of disease, and it is often elicited by low-intensity stimulation or occurs even as resting pain. Causes of pathophysiological pain are either inflammation or injury causing pathophysiological nociceptive pain or damage to nerve cells evoking neuropathic pain. The major peripheral neuronal mechanism of pathophysiological nociceptive pain is the sensitization of peripheral nociceptors for m

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25

Vougiouklis, P. Neural Generation of Textual Summaries from Knowledge Base Triples. IOS Press, Incorporated, 2020.

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26

Levine, Robert. Neural Networks: The Second Ai Generation (Artificial Intelligence Series). McGraw-Hill Companies, 1991.

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27

Seestrom, Susan J. Next Generation Experiments to Measure the Neutron Lifetime: Proceedings of the Workshop. World Scientific Publishing Co Pte Ltd, 2014.

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28

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

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29

Branko, Souček, and IRIS Group, eds. Dynamic, genetic, and chaotic programming: The sixth-generation. Wiley, 1992.

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30

Ole, Kiehn, ed. Neuronal mechanisms for generating locomotor activity. New York Academy of Sciences, 1998.

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31

Lourenço, Nuno, Ricardo Martins, Nuno Horta, and António Gusmão. Analog IC Placement Generation via Neural Networks from Unlabeled Data. Springer, 2020.

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32

Buzanska, Leonora. Human Neural Stem Cells: From Generation to Differentiation and Application. Springer, 2018.

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33

Human Neural Stem Cells: From Generation to Differentiation and Application. Springer, 2018.

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34

Wadman, Wytse J., and Fernando H. Lopes da Silva. Biophysical Aspects of EEG and MEG Generation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0004.

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This chapter reviews the essential physical principles involved in the generation of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. The general laws governing the electrophysiology of neuronal activity are analyzed within the formalism of the Maxwell equations that constitute the basis for understanding electromagnetic fields in general. Three main topics are discussed. The first is the forward problem: How can one calculate the electrical field that results from a known configuration of neuronal sources? The second is the inverse problem: Given an electrical field as

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35

Duman, Ronald S. Neurotrophic Mechanisms of Depression. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0027.

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Early theories of depression and treatment response were centered on the monoamine neurotransmitters, but more recent work has focused on functional and structural synaptic plasticity and the role of neurotrophic factors, particularly brain derived neurotrophic factor (BDNF). Neurotrophic factors regulate all aspects of neuronal function, including adaptive plasticity, synapse formation, and neuronal survival. Chronic stress and depression cause reductions in levels of BDNF and other key factors, including vascular endothelial growth factor (VEGF) and fibroblast growth factor 2 (FGF2), in cort

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36

Blundell, Jacqueline Jeannette, and Evan Preisser, eds. Integrating Predation Risk Across Scales: From Neurons to Ecosystems and Milliseconds to Generations. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-736-2.

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37

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms. O'Reilly Media, 2017.

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38

Fox, Kieran C. R. Neural Origins of Self-Generated Thought. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.1.

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Functional magnetic resonance imaging (fMRI) has begun to narrow down the neural correlates of self-generated forms of thought, with current evidence pointing toward central roles for the default, frontoparietal, and visual networks. Recent work has linked the arising of thoughts more specifically to default network activity, but the limited temporal resolution of fMRI has precluded more detailed conclusions about where in the brain self-created mental content is generated and how this is achieved. This chapter argues that the unparalleled spatiotemporal resolution of intracranial electrophysi

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39

Kandler, Karl, ed. The Oxford Handbook of the Auditory Brainstem. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190849061.001.0001.

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The Oxford Handbook of the Auditory Brainstem provides an in-depth reference to the organization and function of ascending and descending auditory pathways in the mammalian brainstem. Individual chapters are organized along the auditory pathway, beginning with the cochlea and ending with the auditory midbrain. Each chapter provides an introduction to the respective area and summarizes our current knowledge before discussing the disputes and challenges that the field currently faces.The handbook emphasizes the numerous forms of plasticity that are increasingly observed in many areas of the audi

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40

Niyogi, Partha. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar. Springer, 1997.

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41

Han, Shihui. Neural processes of culturally familiar information. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198743194.003.0002.

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Chapter 2 introduces the concept of cultural learning and its function in the transmission of cultural knowledge over generations, and the construction of new cultural beliefs/values and behavioral scripts. It examines brain activity that is engaged in differential processing of culturally familiar and unfamiliar information by reviewing functional magnetic resonance imaging and event-related potential studies of neural activity involved in the processing of gesture, music, brand, and religious knowledge. Long-term cultural experiences give rise to specific neural mechanisms in the human brain

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42

M, Greene N., Oak Ridge National Laboratory, and U.S. Nuclear Regulatory Commission. Division of Systems Analysis and Regulatory Effectiveness., eds. POLIDENT, a module for generating continuous-energy cross sections from ENDF resonance data. U.S. Nuclear Regulatory Commission, 2000.

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43

The Quantum Brain: The Search for Freedom and the Next Generation of Man. Wiley, 2001.

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44

Satinover, Jeffrey. The Quantum Brain: The Search for Freedom and the Next Generation of Man. Wiley, 2002.

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45

Mira, Helena, Aixa Victoria Morales, and Ruth Diez Del Corral, eds. Generation of Neurons and Their Integration in Pre-Existing Circuits in the Postnatal Brain: Signalling in Physiological and Regenerative Contexts. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-988-5.

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46

Manoonpong, Poramate, and Christian Tetzlaff, eds. Neural Computation in Embodied Closed-Loop Systems for the Generation of Complex Behavior: From Biology to Technology. Frontiers Media SA, 2018. http://dx.doi.org/10.3389/978-2-88945-605-5.

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47

Bowman, Ryan, Hannah Schwennesen, Kafui Dzirasa, and Rainbo Hultman. In Vivo Circuit Analysis. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0007.

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Breakthroughs in understanding neural circuit activity hold much promise for developing next generation therapeutics for psychiatric disorders. Determination of how dynamic activity is coordinated across brain regions to effect specific behavioral function (or dysfunction) enables the development of therapeutics with increased specificity and fewer side effects. This chapter discusses methodologies for measuring neural circuit activity in humans and in animal models, and describes a bidirectional research pipeline whereby studies in humans are followed by tightly controlled studies in animal m

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48

Farb, Norman, Philip A. Desormeau, and Le-anh Dinh-Williams. The Neuroscience of Hypo-Egoic Processes. Edited by Kirk Warren Brown and Mark R. Leary. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199328079.013.8.

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The term “hypo-egoic” can refer to a variety of cognitive states, ranging from internal experiences of meditation, hypnosis, or spirituality, to overt acts of forgiveness or altruism. This chapter reviews the nascent literature on the neuroscience supporting such states, aiming to provide a more unified neural account. For parsimony, research findings are framed in terms of implicated brain networks, with particular attention as to whether networks are modulated to directly inhibit of egoic processes, or to generate competing, experientially salient, hypo-egoic states. The chapter concludes th

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49

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 learn

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50

Maggiore, Michele. Gravitational Waves. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.001.0001.

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A comprehensive and detailed account of the physics of gravitational waves and their role in astrophysics and cosmology. The part on astrophysical sources of gravitational waves includes chapters on GWs from supernovae, neutron stars (neutron star normal modes, CFS instability, r-modes), black-hole perturbation theory (Regge-Wheeler and Zerilli equations, Teukoslky equation for rotating BHs, quasi-normal modes) coalescing compact binaries (effective one-body formalism, numerical relativity), discovery of gravitational waves at the advanced LIGO interferometers (discoveries of GW150914, GW15122

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