Computational Neuroscience
Springer International Publishing (Verlag)
978-3-031-75704-4 (ISBN)
- Noch nicht erschienen - erscheint am 29.12.2024
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This book provides an essential introduction to modeling the nervous system at various levels. Readers will learn about the intricate mechanisms of neural activity, receptive fields, neural networks, and information coding.
The chapters cover topics such as membrane potentials, the Hodgkin-Huxley theory, receptive fields and their specificity for important stimulus dimensions, Fourier analysis for neuroscientists, pattern recognition and self-organization in neural networks, and the structure of neural representations. The second edition includes revised text and figures for improved readability and completeness. Key points are highlighted throughout to help readers keep track of central ideas.
Researchers in the field of neuroscience with backgrounds in biology, psychology, or medicine will find this book particularly beneficial. It is also an invaluable reference for all neuroscientists who use computational methods in their daily work. Whether you are a theoretical scientist approaching the field or an experienced practitioner seeking to deepen your understanding, "Computational Neuroscience - An Essential Guide to Membrane Potentials, Receptive Fields, and Neural Networks" offers a comprehensive guide to mastering the fundamentals of this dynamic discipline.
Hanspeter A. Mallot received his PhD from the Faculty of Biology, University of Mainz, Germany, in 1986. In the following years, he held postdoctoral and research positions at the Massachusetts Institute of Technology, the Ruhr-University Bochum, the Max-Planck-Institute for Biological Cybernetics in Tübingen and the Institute for Advanced Study, Berlin. From 2000 to 2023, he headed the cognitive neuroscience unit at the University of Tübingen, where he is currently affiliated as a senior professor. Hanspeter has published more than 100 papers and three books on topics of visual perception, spatial cognition, and neural information processing.
Chapter 1. Excitable Membranes and Neural Conduction.- Chapter 2. Receptive Fields and the Specificity of Neuronal Firing.- Chapter 3. Functional Models of Receptive Fields.- Chapter 4. Fourier Analysis for Neuroscientists.- Chapter 5. Artificial Neural Networks and Classification.- Chapter 6. Artificial Neural Networks With Interacting Output Units.- Chapter 7. Coding and Representation.
| Erscheint lt. Verlag | 29.12.2024 |
|---|---|
| Zusatzinfo | Approx. 250 p. 50 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie ► Studium | |
| Naturwissenschaften ► Biologie ► Humanbiologie | |
| Schlagworte | Artificial Neural Networks • Computer Algorithms • computer codes • Decision Making • Fourier Analysis for Neuroscientists • linear algebra • Membrane potentials • Memory • neural codes • Neuronal Conduction • Neuronal Firing • Self-organization and competitive learning • supervised learning |
| ISBN-10 | 3-031-75704-1 / 3031757041 |
| ISBN-13 | 978-3-031-75704-4 / 9783031757044 |
| Zustand | Neuware |
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