Computational neuroscience a first course /
Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly fo...
| Main Author: | Mallot, Hanspeter A. |
|---|---|
| Other Authors: | SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Cham ; New York :
Springer,
©2013.
Cham ; New York : [2013] |
| Physical Description: |
1 online resource. |
| Series: |
Springer series in bio-/neuroinformatics ;
v. 2. |
| Subjects: |
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| 100 | 1 | |a Mallot, Hanspeter A. | |
| 245 | 1 | 0 | |a Computational neuroscience : |b a first course / |c Hanspeter A Mallot. |
| 260 | |a Cham ; |a New York : |b Springer, |c ©2013. | ||
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| 264 | 4 | |c ©2013. | |
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| 505 | 0 | 0 | |t Excitable Membranes and Neural Conduction -- |t Receptive Fields and the Specificity of Neuronal Firing -- |t Fourier Analysis for Neuroscientists -- |t Artificial Neural Networks -- |t Coding and Representation. |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly for more than ten years to graduate students in neuroscience with backgrounds in biology, psychology and medicine, takes its readers on a journey through three fundamental domains of computational neuroscience: membrane biophysics, systems theory and artificial neural networks. The required mathematical concepts are kept as intuitive and simple as possible throughout the book, making it fully accessible to readers who are less familiar with mathematics. Overall, Computational Neuroscience - A First Course represents an essential reference guide for all neuroscientists who use computational methods in their daily work, as well as for any theoretical scientist approaching the field of computational neuroscience. | ||
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