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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. Birmingham: University of Birmingham, 1991.

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2

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

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3

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

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4

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

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5

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

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6

Neural and concurrent real-time systems: The sixth generation. New York: 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. Cham: 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. New York: Wiley, 1991.

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9

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

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10

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

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11

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

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12

Ahmed, Khalid. A computer model of a neural pattern generator for the canine small intestine. Ottawa: 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. Amsterdam: 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. [New York]: IEEE, 1993.

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15

Ryabov, Vladimir. Oil and Gas Chemistry. ru: 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 environment are considered. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for training in the course "Chemistry of oil and gas", for the preparation of bachelors, masters and certified specialists in the field of training "Oil and Gas business". It can be used for training in other areas in oil and gas universities and be of interest to specialists working in the field of chemistry and technology of oil refining and in other areas of the oil and gas industry.
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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 poised to inform how patients with amyotrophic lateral sclerosis are evaluated and treated in the clinic.
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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

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 be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.
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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 progress in the human iPS cell culture models related to common and rare NDDs, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, spinal muscular atrophy, and degenerative ataxias. It focuses on the pathophysiological features revealed in cell cultures, and the neuronal subtypes most affected in NDDs. The chapter discusses the validity, limitation, and improvements of this system in faithfully and reproducibly recapitulating disease pathology.
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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 second messengers, protein phosphorylation, and RNA and protein synthesis. Moreover, peptides and nucleotides can be injected into individual neurons. Other invertebrate systems such as Drosophila and Caenorhabditis elegans are ideal models for genetic approaches to the exploration of neuronal function and the neuronal bases of behavior. The Oxford Handbook of Invertebrate Neurobiology reviews neurobiological phenomena, including motor pattern generation, mechanisms of synaptic transmission, and learning and memory, as well as circadian rhythms, development, regeneration, and reproduction. Species-specific behaviors are covered in chapters on the control of swimming in annelids, crustacea, and mollusks; locomotion in hexapods; and camouflage in cephalopods. A unique feature of the handbook is the coverage of social behavior and intentionality in invertebrates. These developments are contextualized in a chapter summarizing past contributions of invertebrate research as well as areas for future studies that will continue to advance the field.
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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, intrinsically bursting pacemaker neurons and synaptic network properties. Also covered are the major neuromodulatory and neural control mechanisms. The chapter discusses how a deep knowledge of the stomatogastric circuitry has led to the development of electronic neurons for biomimetic devices that can be used for experimental and prosthetic applications The chapter concludes with a section on new techniques that may help with unraveling oscillatory circuits in the brain.
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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 mechanical, thermal and chemical stimuli; the major peripheral mechanism of neuropathic pain is the generation of ectopic discharges in injured nerve fibres. These phenomena are created by changes of ion channels in the neurons, e.g. by the influence of inflammatory mediators or growth factors. Both peripheral sensitization and ectopic discharges can evoke the development of hyperexcitability of central nociceptive pathways, called central sensitization, which amplifies the nociceptive processing. Central sensitization is caused by changes of the synaptic processing, in which glial cell activation also plays an important role. Endogenous inhibitory neuronal systems may reduce pain but some types of pain are characterized by the loss of inhibitory neural function. In addition to their role in pain generation, nociceptive afferents and the spinal cord can further enhance the inflammatory process by the release of neuropeptides into the innervated tissue and by activation of sympathetic efferent fibres. However, in inflamed tissue the innervation is remodelled by repellent factors, in particular with a loss of sympathetic nerve fibres.
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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 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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29

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

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30

Ole, Kiehn, ed. Neuronal mechanisms for generating locomotor activity. New York: 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 a function of space and time mostly recorded at the scalp (EEG/MEG), how can one reconstruct the underlying generators at the brain level? The third is the reverse problem: How can brain activity be modulated by external electromagnetic fields with diagnostic and/or therapeutic objectives? The chapter emphasizes the importance of understanding the common biophysical framework concerning these three main topics of brain electrical and magnetic activities.
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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 cortical regions that contribute to atrophy and loss of neurons observed in depressed patients and rodent stress models. In contrast, these neurotrophic factors are upregulated by chronic administration of typical antidepressants and are required for antidepressant responses. Moreover, fast acting, highly efficacious antidepressant agents such as ketamine rapidly increase BDNF release and synapse formation, paving the way for a new generation of medications for the treatment of depression.
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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 electrophysiology (iEEG) in human epilepsy patients can begin to provide answers to questions about the specific neural origins of self-generated thought. The chapter reviews the extensive body of literature from iEEG studies over the past few decades and shows that many studies involving passive recording or direct electrical stimulation throughout the brain point to the medial temporal lobe as a key site of thought-generation.
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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 auditory brainstem. Several chapters focus on neuronal modulation of function and plasticity on the synaptic, neuronal, and circuit level, especially during development, aging, and following peripheral hearing loss. In addition, the book addresses the role of trauma-induced maladaptive plasticity with respect to its contribution in generating central hearing dysfunction, such as hyperacusis and tinnitus.The book is intended for students and postdoctoral fellows starting in the auditory field and for researchers of related fields who wish to get an authoritative and up-to-date summary of the current state of auditory brainstem research. For clinical practitioners in audiology, otolaryngology, and neurology, the book is a valuable resource of information about the neuronal mechanisms that are currently discussed as major candidates for the generation of central hearing dysfunction.
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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 that deal with culturally familiar information in multiple neural circuits underlying the inference of mental states and reward, for example. The unique neural mechanisms underlying culturally familiar stimuli provide a default mode of neural processing of culturally familiar information received in daily life.
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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. Washington, DC: 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 models that can then be applied back to humans. Targeted neural circuit manipulations from these studies are already being applied to a wide range of therapeutic strategies.
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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 that hypo-egoic processing is not purely inhibitory in its neural architecture but often incorporates generative neural representations, enhancing sensory awareness in meditation and hypnosis, the theory of another’s mind in love and forgiveness, and vicarious enjoyment in altruistic acts. These generative processes may anchor attention and attenuate prepotent tendencies toward egoic thinking, allowing for the transcendence of self-concern in favor of some greater good.
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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 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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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, GW151226, tests of general relativity, astrophysical implications), supermassive black holes (supermassive black-hole binaries, EMRI, relevance for LISA and pulsar timing arrays). The part on gravitational waves and cosmology include discussions of FRW cosmology, cosmological perturbation theory (helicity decomposition, scalar and tensor perturbations, Bardeen variables, power spectra, transfer functions for scalar and tensor modes), the effects of GWs on the Cosmic Microwave Background (ISW effect, CMB polarization, E and B modes), inflation (amplification of vacuum fluctuations, quantum fields in curved space, generation of scalar and tensor perturbations, Mukhanov-Sasaki equation,reheating, preheating), stochastic backgrounds of cosmological origin (phase transitions, cosmic strings, alternatives to inflation, bounds on primordial GWs) and search of stochastic backgrounds with Pulsar Timing Arrays (PTA).
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