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Computational Neuroscience of Drug Addiction
Drug addiction is one of the most important public health problems in Western societies, and is a rising concern for developing nations. Over the past three decades, experimental research on the neurobiology and psychology of drug addiction has generated a tremendous amount of exciting data, from th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
|
| Edición: | 1st ed. 2012. |
| Colección: | Springer Series in Computational Neuroscience,
10 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Computational Neuroscience of Drug Addiction |h [electronic resource] / |c edited by Boris Gutkin, Serge H. Ahmed. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2012. | |
| 300 | |a XIV, 342 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Springer Series in Computational Neuroscience, |x 2197-1919 ; |v 10 | |
| 505 | 0 | |a Foreword: P Dayan -- Part 1 - Pharmacological-based models of addiction -- Chapter 1. Simple deterministic mathematical Model of maintained drug delf-Administration behavior and its pharmacological applications. V.L. Tsibulsky* and A.B. Norman -- Chapter 2. Intermittent Adaptation : A mathematical model of drug tolerance, dependence and addiction. A. Peper -- Chapter 3. Control theory and addictive behavior. D. Newlin, P.A. Regalia, T.I. Seidman, G. Bobashev -- Part 2 - Neurocomputational models of addiction -- Chapter 4. Circuit models of addiction: receptors and neural dynamics in nicotine self-administration. M. Graupner and B. Gutkin -- Chapter 5. N Dual-system learning models and drugs of abuse. N.D. Daw and D.A. Simon -- Chapter 6. Modeling decision-making systems in addiction. Z. Kurth-Nelson and A. D. Redish -- Chapter 7. Computational models of incentive-sensitization in addiction: Dynamic limbic transformation of learning into motivation. J. Zhang, K. C. Berridge, and J. W. Aldridge -- Chapter 8. Understanding addiction as a pathological state of multiple decision making processes: a neurocomputational perspective. M. Keramati, A. Dezfouli and P. Piray -- Part 3 - Economic-based models of addiction -- Chapter 9. Policies and priors. K Friston -- Chapter 10. Toward a Computationally Unified Behavioral-Economic Model of Addiction. E.T. Mueller, L.P. Carter and W.K. Bickel -- Chapter 11. Simulating Patterns of Heroin Addiction within the Social Context of a Local Heroin Market. L. Hoffer, G. Bobashev and R. J Morris. | |
| 520 | |a Drug addiction is one of the most important public health problems in Western societies, and is a rising concern for developing nations. Over the past three decades, experimental research on the neurobiology and psychology of drug addiction has generated a tremendous amount of exciting data, from the molecular to the behavioral levels. As a result, a new and pressing challenge for addiction research is to formulate a synthetic theoretical framework that goes well beyond mere scientific eclecticism to deepen our understanding of drug addiction and to foster our capacity to prevent and to cure drug addiction. Recently, researchers from a broad spectrum of scientific disciplines - from pharmacokinetics to neuroeconomics - have begun to take up this challenge. Computational Neuroscience of Drug Addiction collects recent mathematical and neurocomputational modeling approaches to drug self-administration and addiction. This selection of chapters illustrates how mathematical modeling can contribute to our understanding of the mechanisms underlying the transition between the different stages of addiction, from initiation of regulated drug use and subsequent loss of control over drug use to relapse after long-term abstinence. The volume provides an overview of this emerging area and will serve as a standard reference for addiction researchers and quantitative scientists entering the field. . | ||
| 650 | 0 | |a Neurosciences. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 1 | 4 | |a Neuroscience. |
| 650 | 2 | 4 | |a Computational and Systems Biology. |
| 700 | 1 | |a Gutkin, Boris. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Ahmed, Serge H. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781461407508 |
| 776 | 0 | 8 | |i Printed edition: |z 9781461429401 |
| 776 | 0 | 8 | |i Printed edition: |z 9781461407522 |
| 830 | 0 | |a Springer Series in Computational Neuroscience, |x 2197-1919 ; |v 10 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4614-0751-5 |z Texto Completo |
| 912 | |a ZDB-2-SBL | ||
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| 950 | |a Biomedical and Life Sciences (SpringerNature-11642) | ||
| 950 | |a Biomedical and Life Sciences (R0) (SpringerNature-43708) | ||