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Mathematical foundations of neuroscience / G. Bard Ermentrout, David H. Terman
| Mathematical foundations of neuroscience / G. Bard Ermentrout, David H. Terman |
| Autore | Ermentrout, Bard |
| Pubbl/distr/stampa | New York [etc.], : Springer, 2010 |
| Descrizione fisica | XV, 422 p. ; 24 cm |
| Disciplina |
612.8
612.80151 |
| Altri autori (Persone) | Terman, David Hillel |
| Collana | Interdisciplinary applied mathematics |
| ISBN | 9780387877075 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNISANNIO-PMI0009527 |
Ermentrout, Bard
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| New York [etc.], : Springer, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Sannio | ||
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Mathematical tools for neuroscience : a geometric approach / / Richard A. Clement
| Mathematical tools for neuroscience : a geometric approach / / Richard A. Clement |
| Autore | Clement Richard A. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (168 pages) |
| Disciplina | 612.80151 |
| Collana | Lecture Notes in Morphogenesis |
| Soggetto topico |
Brain - Mathematical models
Manifolds (Mathematics) Neurociències Matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030984953
9783030984946 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996472037903316 |
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Clement Richard A.
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Mathematical Tools for Neuroscience : A Geometric Approach / / by Richard A. Clement
| Mathematical Tools for Neuroscience : A Geometric Approach / / by Richard A. Clement |
| Autore | Clement Richard A. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (168 pages) |
| Disciplina |
612.80151
612.820151607 |
| Collana | Lecture Notes in Morphogenesis |
| Soggetto topico |
Biomathematics
Sensorimotor cortex Computational neuroscience Biometry Mathematical and Computational Biology Sensorimotor Processing Computational Neuroscience Biostatistics |
| ISBN |
9783030984953
9783030984946 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910564698003321 |
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Clement Richard A.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematics for neuroscientists [[electronic resource] /] / Fabrizio Gabbiani, Steven J. Cox
| Mathematics for neuroscientists [[electronic resource] /] / Fabrizio Gabbiani, Steven J. Cox |
| Autore | Gabbiani Fabrizio |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Elsevier, 2010 |
| Descrizione fisica | 1 online resource (505 p.) |
| Disciplina |
612.8
612.80151 |
| Altri autori (Persone) | CoxSteven J |
| Collana | Elsevier science & technology books |
| Soggetto topico |
Medicine - Mathematics
Neurosciences |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-76902-2
9786612769023 0-08-089049-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Mathematics for Neuroscientists; Copyright page; Full Contents; Preface; Chapter 1. Introduction; 1.1. How to Use This Book; 1.2. Brain Facts Brief; 1.3. Mathematical Preliminaries; 1.4. Units; 1.5. Sources; Chapter 2. The Passive Isopotential Cell; 2.1. Introduction; 2.2. The Nernst Potential; 2.3. Membrane Conductance; 2.4. Membrane Capacitance and Current Balance; 2.5. Synaptic Conductance; 2.6. Summary and Sources; 2.7. Exercises; Chapter 3. Differential Equations; 3.1. Exact Solution; 3.2. Moment Methods*; 3.3. The Laplace Transform*; 3.4. Numerical Methods
3.5. Synaptic Input 3.6. Summary and Sources; 3.7. Exercises; Chapter 4. The Active Isopotential Cell; 4.1. The Delayed Rectifier Potassium Channel; 4.2. The Sodium Channel; 4.3. The Hodgkin-Huxley Equations; 4.4. The Transient Potassium Channel*; 4.5. Summary and Sources; 4.6. Exercises; Chapter 5. The Quasi-Active Isopotential Cell; 5.1. The Quasi-Active Model; 5.2. Numerical Methods; 5.3. Exact Solution via Eigenvector Expansion; 5.4. A Persistent Sodium Current*; 5.5. A Nonspecific Cation Current that is Activated by Hyperpolarization*; 5.6. Summary and Sources; 5.7. Exercises Chapter 6. The Passive Cable 6.1. The Discrete Passive Cable Equation; 6.2. Exact Solution Via Eigenvector Expansion; 6.3. Numerical Methods; 6.4. The Passive Cable Equation; 6.5. Synaptic Input; 6.6. Summary and Sources; 6.7. Exercises; Chapter 7. Fourier Series and Transforms; 7.1. Fourier Series; 7.2. The Discrete Fourier Transform; 7.3. The Continuous Fourier Transform; 7.4. Reconciling the Discrete and Continuous Fourier Transforms; 7.5. Summary and Sources; 7.6. Exercises; Chapter 8. The Passive Dendritic Tree; 8.1. The Discrete Passive Tree; 8.2. Eigenvector Expansion 8.3. Numerical Methods 8.4. The Passive Dendrite Equation; 8.5. The Equivalent Cylinder*; 8.6. Branched Eigenfunctions*; 8.7. Summary and Sources; 8.8. Exercises; Chapter 9. The Active Dendritic Tree; 9.1. The Active Uniform Cable; 9.2. On the Interaction of Active Uniform Cables*; 9.3. The Active Nonuniform Cable; 9.4. The Quasi-Active Cable*; 9.5. The Active Dendritic Tree; 9.6. Summary and Sources; 9.7. Exercises; Chapter 10. Reduced Single Neuron Models; 10.1. The Leaky Integrate-and-Fire Neuron; 10.2. Bursting Neurons; 10.3. Simplified Models of Bursting Neurons; 10.4. Summary and Sources 10.5. Exercises Chapter 11. Probability and Random Variables; 11.1. Events and Random Variables; 11.2. Binomial Random Variables; 11.3. Poisson Random Variables; 11.4. Gaussian Random Variables; 11.5. Cumulative Distribution Functions; 11.6. Conditional Probabilities*; 11.7. Sum of Independent Random Variables*; 11.8. Transformation of Random Variables*; 11.9. Random Vectors*; 11.10. Exponential and Gamma Distributed Random Variables; 11.11. The Homogeneous Poisson Process; 11.12. Summary and Sources; 11.13. Exercises; Chapter 12. Synaptic Transmission and Quantal Release 12.1. Basic Synaptic Structure and Physiology |
| Record Nr. | UNINA-9910457975403321 |
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Gabbiani Fabrizio
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| Amsterdam ; ; Boston, : Elsevier, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematics for neuroscientists [[electronic resource] /] / Fabrizio Gabbiani, Steven J. Cox
| Mathematics for neuroscientists [[electronic resource] /] / Fabrizio Gabbiani, Steven J. Cox |
| Autore | Gabbiani Fabrizio |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Elsevier, 2010 |
| Descrizione fisica | 1 online resource (505 p.) |
| Disciplina |
612.8
612.80151 |
| Altri autori (Persone) | CoxSteven J |
| Collana | Elsevier science & technology books |
| Soggetto topico |
Medicine - Mathematics
Neurosciences |
| ISBN |
1-282-76902-2
9786612769023 0-08-089049-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Mathematics for Neuroscientists; Copyright page; Full Contents; Preface; Chapter 1. Introduction; 1.1. How to Use This Book; 1.2. Brain Facts Brief; 1.3. Mathematical Preliminaries; 1.4. Units; 1.5. Sources; Chapter 2. The Passive Isopotential Cell; 2.1. Introduction; 2.2. The Nernst Potential; 2.3. Membrane Conductance; 2.4. Membrane Capacitance and Current Balance; 2.5. Synaptic Conductance; 2.6. Summary and Sources; 2.7. Exercises; Chapter 3. Differential Equations; 3.1. Exact Solution; 3.2. Moment Methods*; 3.3. The Laplace Transform*; 3.4. Numerical Methods
3.5. Synaptic Input 3.6. Summary and Sources; 3.7. Exercises; Chapter 4. The Active Isopotential Cell; 4.1. The Delayed Rectifier Potassium Channel; 4.2. The Sodium Channel; 4.3. The Hodgkin-Huxley Equations; 4.4. The Transient Potassium Channel*; 4.5. Summary and Sources; 4.6. Exercises; Chapter 5. The Quasi-Active Isopotential Cell; 5.1. The Quasi-Active Model; 5.2. Numerical Methods; 5.3. Exact Solution via Eigenvector Expansion; 5.4. A Persistent Sodium Current*; 5.5. A Nonspecific Cation Current that is Activated by Hyperpolarization*; 5.6. Summary and Sources; 5.7. Exercises Chapter 6. The Passive Cable 6.1. The Discrete Passive Cable Equation; 6.2. Exact Solution Via Eigenvector Expansion; 6.3. Numerical Methods; 6.4. The Passive Cable Equation; 6.5. Synaptic Input; 6.6. Summary and Sources; 6.7. Exercises; Chapter 7. Fourier Series and Transforms; 7.1. Fourier Series; 7.2. The Discrete Fourier Transform; 7.3. The Continuous Fourier Transform; 7.4. Reconciling the Discrete and Continuous Fourier Transforms; 7.5. Summary and Sources; 7.6. Exercises; Chapter 8. The Passive Dendritic Tree; 8.1. The Discrete Passive Tree; 8.2. Eigenvector Expansion 8.3. Numerical Methods 8.4. The Passive Dendrite Equation; 8.5. The Equivalent Cylinder*; 8.6. Branched Eigenfunctions*; 8.7. Summary and Sources; 8.8. Exercises; Chapter 9. The Active Dendritic Tree; 9.1. The Active Uniform Cable; 9.2. On the Interaction of Active Uniform Cables*; 9.3. The Active Nonuniform Cable; 9.4. The Quasi-Active Cable*; 9.5. The Active Dendritic Tree; 9.6. Summary and Sources; 9.7. Exercises; Chapter 10. Reduced Single Neuron Models; 10.1. The Leaky Integrate-and-Fire Neuron; 10.2. Bursting Neurons; 10.3. Simplified Models of Bursting Neurons; 10.4. Summary and Sources 10.5. Exercises Chapter 11. Probability and Random Variables; 11.1. Events and Random Variables; 11.2. Binomial Random Variables; 11.3. Poisson Random Variables; 11.4. Gaussian Random Variables; 11.5. Cumulative Distribution Functions; 11.6. Conditional Probabilities*; 11.7. Sum of Independent Random Variables*; 11.8. Transformation of Random Variables*; 11.9. Random Vectors*; 11.10. Exponential and Gamma Distributed Random Variables; 11.11. The Homogeneous Poisson Process; 11.12. Summary and Sources; 11.13. Exercises; Chapter 12. Synaptic Transmission and Quantal Release 12.1. Basic Synaptic Structure and Physiology |
| Record Nr. | UNINA-9910791474803321 |
|
Gabbiani Fabrizio
|
||
| Amsterdam ; ; Boston, : Elsevier, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematics for neuroscientists / / Fabrizio Gabbiani, Steven J. Cox
| Mathematics for neuroscientists / / Fabrizio Gabbiani, Steven J. Cox |
| Autore | Gabbiani Fabrizio |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Elsevier, 2010 |
| Descrizione fisica | 1 online resource (505 p.) |
| Disciplina |
612.8
612.80151 |
| Altri autori (Persone) | CoxSteven J |
| Collana | Elsevier science & technology books |
| Soggetto topico |
Medicine - Mathematics
Neurosciences |
| ISBN |
9786612769023
9781282769021 1282769022 9780080890494 0080890490 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Mathematics for Neuroscientists; Copyright page; Full Contents; Preface; Chapter 1. Introduction; 1.1. How to Use This Book; 1.2. Brain Facts Brief; 1.3. Mathematical Preliminaries; 1.4. Units; 1.5. Sources; Chapter 2. The Passive Isopotential Cell; 2.1. Introduction; 2.2. The Nernst Potential; 2.3. Membrane Conductance; 2.4. Membrane Capacitance and Current Balance; 2.5. Synaptic Conductance; 2.6. Summary and Sources; 2.7. Exercises; Chapter 3. Differential Equations; 3.1. Exact Solution; 3.2. Moment Methods*; 3.3. The Laplace Transform*; 3.4. Numerical Methods
3.5. Synaptic Input 3.6. Summary and Sources; 3.7. Exercises; Chapter 4. The Active Isopotential Cell; 4.1. The Delayed Rectifier Potassium Channel; 4.2. The Sodium Channel; 4.3. The Hodgkin-Huxley Equations; 4.4. The Transient Potassium Channel*; 4.5. Summary and Sources; 4.6. Exercises; Chapter 5. The Quasi-Active Isopotential Cell; 5.1. The Quasi-Active Model; 5.2. Numerical Methods; 5.3. Exact Solution via Eigenvector Expansion; 5.4. A Persistent Sodium Current*; 5.5. A Nonspecific Cation Current that is Activated by Hyperpolarization*; 5.6. Summary and Sources; 5.7. Exercises Chapter 6. The Passive Cable 6.1. The Discrete Passive Cable Equation; 6.2. Exact Solution Via Eigenvector Expansion; 6.3. Numerical Methods; 6.4. The Passive Cable Equation; 6.5. Synaptic Input; 6.6. Summary and Sources; 6.7. Exercises; Chapter 7. Fourier Series and Transforms; 7.1. Fourier Series; 7.2. The Discrete Fourier Transform; 7.3. The Continuous Fourier Transform; 7.4. Reconciling the Discrete and Continuous Fourier Transforms; 7.5. Summary and Sources; 7.6. Exercises; Chapter 8. The Passive Dendritic Tree; 8.1. The Discrete Passive Tree; 8.2. Eigenvector Expansion 8.3. Numerical Methods 8.4. The Passive Dendrite Equation; 8.5. The Equivalent Cylinder*; 8.6. Branched Eigenfunctions*; 8.7. Summary and Sources; 8.8. Exercises; Chapter 9. The Active Dendritic Tree; 9.1. The Active Uniform Cable; 9.2. On the Interaction of Active Uniform Cables*; 9.3. The Active Nonuniform Cable; 9.4. The Quasi-Active Cable*; 9.5. The Active Dendritic Tree; 9.6. Summary and Sources; 9.7. Exercises; Chapter 10. Reduced Single Neuron Models; 10.1. The Leaky Integrate-and-Fire Neuron; 10.2. Bursting Neurons; 10.3. Simplified Models of Bursting Neurons; 10.4. Summary and Sources 10.5. Exercises Chapter 11. Probability and Random Variables; 11.1. Events and Random Variables; 11.2. Binomial Random Variables; 11.3. Poisson Random Variables; 11.4. Gaussian Random Variables; 11.5. Cumulative Distribution Functions; 11.6. Conditional Probabilities*; 11.7. Sum of Independent Random Variables*; 11.8. Transformation of Random Variables*; 11.9. Random Vectors*; 11.10. Exponential and Gamma Distributed Random Variables; 11.11. The Homogeneous Poisson Process; 11.12. Summary and Sources; 11.13. Exercises; Chapter 12. Synaptic Transmission and Quantal Release 12.1. Basic Synaptic Structure and Physiology |
| Record Nr. | UNINA-9910965855103321 |
|
Gabbiani Fabrizio
|
||
| Amsterdam ; ; Boston, : Elsevier, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
