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1

Maeland, Jahn Andreas. Model-reference neural control. Salford: University of Salford, 1994.

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2

Ferrell, William R., and Uwe Proske. Neural control of movement. New York: Springer Science+Business Media, LLC, 1995.

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3

Moshou, Dimitrios. Neural control of robot. Manchester: UMIST, 1993.

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4

Ferrell, William R., and Uwe Proske, eds. Neural Control of Movement. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1985-0.

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5

P, Banks Stephen. Optimal control by neural networks. Sheffield: Universityof Sheffield, Dept. of Control Engineering, 1990.

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6

Ge, S. S. Stable Adaptive Neural Network Control. Boston, MA: Springer US, 2002.

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7

Ge, Shuzhi S., Chang C. Hang, Tong H. Lee, and Tao Zhang. Stable Adaptive Neural Network Control. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-6577-9.

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8

Willems, Timotheus Martinus. Neural networks in control?: Proefschrift. Eindhoven: Technische Universiteit Eindhoven, 1993.

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9

The neural basis of motor control. New York: Oxford University Press, 1986.

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10

Nie, Junhong. Fuzzy-neural control: Principles, algorithms andapplications. New York: Prentice-Hall, 1995.

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11

Clark, Christopher M. Neural network algorithms for robot control. Ottawa: National Library of Canada, 1998.

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12

Hunn, Luke J. Neural control of a simple robot. Manchester: UMIST, 1994.

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13

Sanchez, Edgar N., Alma Y. Alanís, and Alexander G. Loukianov. Discrete-Time High Order Neural Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78289-6.

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14

Randall, M. Adaptive neural control of walking robots. London: Professional Engineering, 2001.

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15

Garcia-Hernandez, Ramon, Michel Lopez-Franco, Edgar N. Sanchez, Alma y. Alanis, and Jose A. Ruz-Hernandez. Decentralized Neural Control: Application to Robotics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53312-4.

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16

Kulawski, Grzegorz Jacek. Neural networks for nonlinear adaptive control. Birmingham: University of Birmingham, 1997.

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17

Chiu, Alan Wing Lun. Hybrid neural networks: Using artificial neural networks for the analysis and control of biological neural networks. Ottawa: National Library of Canada, 2002.

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18

1958-, Liu Xing, ed. Neural networks for identification, prediction, and control. London: Springer-Verlag, 1995.

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19

Hunt, Kenneth J. Neural Network Engineering in Dynamic Control Systems. London: Springer London, 1995.

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20

1952-, Watanabe Keigo, ed. Intelligent control based on flexible neural networks. Dordrecht: Kluwer Academic Publishers, 1999.

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21

Teshnehlab, Mohammad. Intelligent Control Based on Flexible Neural Networks. Dordrecht: Springer Netherlands, 1999.

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22

Menke, Kurt William. Nonlinear adaptive control using backpropagating neural networks. Monterey, Calif: Naval Postgraduate School, 1992.

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23

Scott, Russell W. Applications of neural networks to adaptive control. Monterey, Calif: Naval Postgraduate School, 1989.

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24

Neural basis of motivational and cognitive control. Cambridge, Mass: MIT Press, 2012.

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25

Nie, Junhong. Fuzzy-neural control: Principles, algorithms, and applications. New York: Prentice Hall, 1995.

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26

Teshnehlab, Mohammad, and Keigo Watanabe. Intelligent Control Based on Flexible Neural Networks. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9187-4.

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27

Hunt, Kenneth J., George R. Irwin, and Kevin Warwick, eds. Neural Network Engineering in Dynamic Control Systems. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3066-6.

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28

Zoppoli, Riccardo, Marcello Sanguineti, Giorgio Gnecco, and Thomas Parisini. Neural Approximations for Optimal Control and Decision. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29693-3.

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29

Sánchez, Edgar N., and Larbi Djilali. Neural Control of Renewable Electrical Power Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47443-0.

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30

C, Ritter Robert, Ritter Sue, and Barnes Charles D. 1935-, eds. Feeding behavior: Neural and humoral controls. Orlando: Academic Press, 1986.

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31

C, Ritter Robert, Ritter Sue, and Barnes Charles D. 1935-, eds. Feeding behavior: Neural and humoral controls. New York: Academic Press, 1986.

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32

Feeding Behavior Neural and Humoral Controls. Elsevier, 1986. http://dx.doi.org/10.1016/b978-0-12-589060-1.x5001-9.

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33

Society of Automotive Engineers. World Congress, ed. Electronic engine controls 2001: Modeling, controls, OBD, and neural networks. Warrendale, PA: Society of Automotive Engineers, 2001.

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34

Engineers, Society of Automotive, and SAE International Congress & Exposition (1999 : Detroit, Mich.), eds. Electronic engine controls 1999: Neural networks, diagnostic and electronic hardware, and controls. Warrendale, Pa: Society of Automotive Engineers, 1999.

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35

Calingasan, Noel Y. Neural substrates of metabolic controls of feeding behavior. 1992.

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36

Electronic Engine Controls 2002: Engine Control, Neural Networks and Non-Linear Systems. Society of Automotive Engineers Inc, 2002.

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37

Electronic Engine Controls 1999: Neural Networks, Diagnostic and Electronic Hardware, and Controls (Special Publications). SAE International, 1999.

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38

Electronic Engine Controls 2001 Modeling, Controls, Obo & Neural Networks: Sae 2001 World Congress Conference. Society of Automotive Engineers Inc, 2001.

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39

Ritter, Robert C., and Sue Ritter. Feeding Behavior: Neural and Humoral Controls (Research topics in physiology). Academic Pr, 1987.

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40

Engineers, Society of Automotive, and Society of Automotive Engineers. World Congress, eds. Electronic engine controls 2000: Modeling, neural networks, OBD, and sensors. Warrendale, Pa: Society of Automotive Engineers, 2000.

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41

Ritter, Robert C., and Sue Ritter. Feeding Behavior: Neural and Humoral Controls (Research topics in physiology). Academic Pr, 1987.

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42

Tang, Kenneth K. L. Neural correlates of short-term somatosensory skill learning in healthy controls. 2006.

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43

Engineers, Society of Automotive, and SAE International Congress & Exposition (1999 : Detroit, Mich.), eds. Electronic engine controls 1999: Sensors, actuators, and development tools. Warrendale, Pa: Society of Automotive Engineers, 1999.

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44

Electronic Engine Controls 1999: Sensors, Actuators, and Development Tools (Special Publications). SAE International, 1999.

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45

Society of Automotive Engineers. World Congress, ed. Electronic engine controls 2001: Censors and actuators, hardware, tools, and validation. Warrendale, PA: Society of Automotive Engineers, 2001.

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46

Electronic Engine Controls 2001: Sensors and Actuators, Hardware, Tools and Validation. Society of Automotive Engineers Inc, 2001.

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47

Electronic Engine Controls 2000: Modeling, Neural Networks, Obd, and Sensors (S P (Society of Automotive Engineers)). Society of Automotive Engineers Inc, 2000.

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48

Schiff, Steven J. Neural Control Engineering. The MIT Press, 2011. http://dx.doi.org/10.7551/mitpress/8436.001.0001.

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49

Bechara, Antoine. Impulse Control Disorders in Neurological Settings. Edited by Jon E. Grant and Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0126.

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This chapter will argue that impulse control disorders, including addiction, are the product of an imbalance between two separate but interacting neural systems: (1) an impulsive amygdala-striatum–dependent neural system that promotes automatic and habitual behaviors and (2) a reflective prefrontal cortex–dependent neural system for decision making, forecasting the future consequences of a behavior, and inhibitory control. The reflective system controls the impulsive system via several mechanisms. However, this control is not absolute; hyperactivity within the impulsive system can override the reflective system. While most prior research has focused on the impulsive system (especially the ventral striatum and its mesolimbic dopamine projection) in promoting the motivation and drive to seek drugs, or on the reflective system (prefrontal cortex) and its mechanisms for decision making and impulse control, more recent evidence suggests that a largely overlooked structure, namely the insula, plays a key role in maintaining poor impulse control, including addiction. This review highlights the potential functional role the insula plays in addiction. We propose that the insula translates bottom-up, interoceptive signals into what subjectively may be experienced as an urge or craving, which in turn potentiates the activity of the impulsive system and/or weakens or hijacks the goal-driven cognitive resources that are needed for the normal operation of the reflective system.
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50

R, Ferrell William, Proske Uwe, and International Union of Physiological Sciences. Congress, eds. Neural control of movement. New York: Plenum Press, 1995.

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