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Books on the topic 'Feedback neuron'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Ansari, Mohd Samar. Non-Linear Feedback Neural Networks. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1563-9.

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2

1973-, Garces Freddy, ed. Strategies for feedback linearisation: A dynamic neural network approach. London: Springer, 2003.

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3

Kwan, Hon C. Network relaxation as behavioral action: Some conjectures on the control of movement by the nervous system. Toronto: University of Toronto, Dept. of Physiology, Computer Science and Anatomy, 1988.

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4

Kuridan, Ramadan Muftah. Computational neutron transport and thermal-hydraulics feedback and transient models for the safe integral reactor concept. Birmingham: University of Birmingham, 1995.

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5

Marios, Polycarpou, ed. Adaptive approximation based control: Unifying neural, fuzzy and traditional adaptive approximation approaches. Hoboken, NJ: Wiley, 2006.

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6

Suresh, Jagannathan, and Yeşildirek A, eds. Neural network control of robot manipulators and nonlinear systems. London: Taylor & Francis, 1999.

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7

Leigh, J. R. Control Theory. 2nd ed. Stevenage: IET, 2004.

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8

Montgomery, Erwin B. Discrete Neural Oscillators. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259600.003.0017.

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The therapeutic mechanisms of action of DBS likely involve neural and neuronal oscillators. “Neuronal oscillators” describes periodic fluctuations of electrical potentials across the neuronal membrane, particularly in the soma, which is reflected in an action-potential-initiating segment. “Neural oscillators” describes closed loop (feedback) multi-neuronal polysynaptic circuits, on account of the propagations of action potentials through the circuit. Neural oscillators are the focus of this chapter. The features, properties and dyanmics introduced in Chapter 16 – Basic Oscillators are extended from continuous harmonic oscillators to discrete neural oscillators. While discrete oscillators received scant attention to date, systems of discrete oscillators have much richer set of dynamics that could provide better understanding of the pathophysiology and physiology of neural systems, such as the basal ganglia-thalamic-cortical system as well as greater insights into the therapeutic mechanisms of action underlying DBS.
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9

Fyfe, Colin. Hebbian Learning and Negative Feedback Networks. Springer, 2010.

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10

Fyfe, Colin. Hebbian Learning and Negative Feedback Networks. Springer London, Limited, 2007.

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11

Warwick, Kevin, Freddy Rafael Garces, Victor Manuel Becerra, and Chandrasekhar Kambhampati. Strategies for Feedback Linearisation. Springer, 2003.

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12

Ansari, Mohd Samar. Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. Springer, 2013.

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13

Ansari, Mohd Samar. Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. Springer London, Limited, 2013.

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14

Warwick, Kevin, Freddy Rafael Garces, Victor Manuel Becerra, and Chandrasekhar Kambhampati. Strategies for Feedback Linearisation: A Dynamic Neural Network Approach. Springer London, Limited, 2012.

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15

Ansari, Mohd Samar Samar. Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. Springer, 2016.

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16

Fyfe, Colin. Hebbian Learning and Negative Feedback Networks (Advanced Information and Knowledge Processing). Springer, 2004.

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17

S, Litt Jonathan, Guo Ten-Huei, and Lewis Research Center, eds. Neural network-based sensor validation for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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18

S, Litt Jonathan, Guo Ten-Huei, and Lewis Research Center, eds. Neural network-based sensor validation for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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19

S, Litt Jonathan, Guo Ten-Huei, and Lewis Research Center, eds. Neural network-based sensor validation for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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20

S, Litt Jonathan, Guo T. H, and Lewis Research Center, eds. Neural network-based sensor validation for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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21

High-Level Feedback Control With Neural Networks (World Scientific Series in Robotics and Intelligent Systems, Vol 21). World Scientific Publishing Company, 1999.

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22

Huang, Jie, Murad Abu-Khalaf, and Frank L. Lewis. Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks. Springer, 2010.

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23

Huang, Jie, Murad Abu-Khalaf, and Frank L. Lewis. Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks. Springer, 2006.

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24

Han, Shihui. Cultural differences in neurocognitive processing of others. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198743194.003.0005.

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Chapter 5 examines cross-cultural neuroimaging studies of neural processes underlying social interactions. East Asian and Western cultural experiences produce specific cognitive and neural strategies in perception of face and expression, empathy for others’ emotional states, regulation of one’s own emotion, understanding others’ beliefs, perception of others’ social status, and processing of social feedback. The cultural differences in neurocognitive processing of others have been observed in most part of the social brain network, covering both cortical and subcortical structures, and support culturally specific behavior.
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25

Biewener, Andrew A., and Shelia N. Patek, eds. Neuromuscular Control of Movement. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198743156.003.0008.

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The control of movement is essential for animals traversing complex environments and operating across a range of speeds and gaits. We consider how animals process sensory information and initiate motor responses, primarily focusing on simple motor responses that involve local reflex pathways of feedback and control, rather than the more complex, longer-term responses that require the broader integration of higher centers within the nervous system. We explore how local circuits facilitate decentralized coordination of locomotor rhythm and examine the fundamentals of sensory receptors located in the muscles, tendons, joints, and at the animal’s body surface. These sensors monitor the animal’s physical environment and the action of its muscles. The sensory information is then carried back to the animal’s nervous system by afferent neurons, providing feedback that is integrated at the level of the spinal cord of vertebrates and sensory-motor ganglia of invertebrates.
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26

Huang, Jie, Murad Abu-Khalaf, and Frank L. Lewis. Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks (Advances in Industrial Control). Springer, 2006.

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27

Brogaard, Berit. Synesthetic Binding and the Reactivation Model of Memory. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199688289.003.0007.

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Despite the recent surge in research on, and interest in, synesthesia, the mechanism underlying this condition is still unknown. Feedforward mechanisms involving overlapping receptive fields of sensory neurons as well as feedback mechanisms involving a lack of signal disinhibition have been proposed. Here I show that a broad range of studies of developmental synesthesia indicate that the mechanism underlying the phenomenon may in some cases involve the reinstatement of brain activity in sensory or cognitive streams in a way that is similar to what happens during memory retrieval of semantically associated items. In the chapter’s final sections I look at the relevance of synesthesia research, given the memory model, to our understanding of multisensory perception and common mapping patterns.
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28

J, Shen, Grohs J. R, and United States. National Aeronautics and Space Administration., eds. Hypersonic vehicle trajectory optimization and control: Final report, grant number--NAG 1 1728. [Washington, DC: National Aeronautics and Space Administration, 1997.

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29

Hypersonic vehicle trajectory optimization and control: Final report, grant number--NAG 1 1728. [Washington, DC: National Aeronautics and Space Administration, 1997.

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30

Lewis, F. W., S. Jagannathan, and A. Yesildirak. Neural Network Control of Robot Manipulators and Non-Linear Systems. Taylor & Francis Group, 2020.

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31

Lewis, F. W., S. Jagannathan, and A. Yesildirak. Neural Network Control of Robot Manipulators and Non-Linear Systems. Taylor & Francis Group, 2020.

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32

Lewis, F. W., S. Jagannathan, and A. Yesildirak. Neural Network Control of Robot Manipulators and Non-Linear Systems. Taylor & Francis Group, 2020.

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33

Lewis, F. W., S. Jagannathan, and A. Yesildirak. Neural Network Control of Robot Manipulators and Non-Linear Systems. Taylor & Francis Group, 2020.

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34

Lewis, F. W., S. Jagannathan, and A. Yesildirak. Neural Network Control Of Robot Manipulators And Non-Linear Systems (Series in Systems and Control). CRC, 1998.

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35

Altenmüller, Eckart, and Sabine Schneider. Planning and performance. Edited by Susan Hallam, Ian Cross, and Michael Thaut. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199298457.013.0031.

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Making music is one of the most demanding tasks for the human central nervous system. It involves the precise execution of very fast and, in many instances, extremely complex physical movements that must be coordinated with continuous auditory feedback. Practice is required to develop these skills and carry out these complex tasks. This article discusses the neurobiological foundations of planning, motor learning, and practice. The first section introduces essential general information for musical readers concerning the organization of cortical, subcortical, and cerebellar motor systems in the brain. The second section addresses the brain processes during acquisition of skilled movements in music making and demonstrates the dynamics of neuronal networks. The third section reports new findings on practice strategies and performance quality. The fourth section presents the causes of degradation of skilled movements in professional musicians. The article concludes with some comments concerning the significance of results of brain research in order to improve practice habits and performance in musicians.
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36

Sherman, S. Murray, and W. Martin Usrey. Exploring Thalamocortical Interactions. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780197503874.001.0001.

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The thalamus and cerebral cortex are active and necessary partners in the processing of signals essential for sensory, motor, and cognitive functions. This partnership is absolute, as neither the thalamus nor the cortex can be understood in any meaningful way in isolation from the other. This book provides readers with fundamental knowledge about the cells and circuits that mediate thalamocortical interactions and then explores new ideas that often challenge conventional understanding. Some of the major themes emphasized throughout the book include the need for a proper classification of thalamocortical and corticothalamic circuits, the role of spike timing for thalamocortical and corticothalamic communication and the mechanisms for modulating spike timing, the organization and function of corticothalamic feedback projections, the role of higher order thalamic nuclei in cortico-cortical communication and cortical functioning, attentional modulation of thalamocortical interactions, and a rethinking of efference copies and distinguishing neural signals as sensory versus motor. Importantly, to encourage readers to think beyond the material and views provided throughout the book, each chapter closes with a section on “Some Outstanding Questions” to stimulate creative approaches to increase our understanding of thalamocortical interactions.
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37

Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.
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