Spiking Neuron Models

This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.

List of contents

1. Introduction; Part I. Single Neuron Models: 2. Detailed neuron models; 3. Two-dimensional neuron models; 4. Formal spiking neuron models; 5. Noise in spiking neuron models; Part II. Population Models: 6. Population equations; 7. Signal transmission and neuronal coding; 8. Oscillations and synchrony; 9. Spatially structured networks; Part III. Models of Synaptic Plasticity: 10. Hebbian models; 11. Learning equations; 12. Plasticity and coding; Bibliography; Index.

Summary

Aimed at courses in computational neuroscience, theoretical biology, biophysics, or neural networks, this 2002 text will suit students of physics, mathematics, or computer science, as well as biologists who are interested in mathematical modelling. A large number of worked examples are embedded in the profusely-illustrated text.

Product details

• Authors: W Gerstner, Wulfram Gerstner, Wulfram (Ecole Polytechnique Federale de Lausanne) Gerstner, Gerstner Wulfram, W Kistler, Werner Kistler, Werner M. Kistler, Werner M. (Erasmus Universiteit Rotterdam) Kistler, Kistler Werner M.
• Publisher: Cambridge University Press Academic
• Languages: English
• Product format: Paperback / Softback
• Released: 15.08.2002
• EAN: 9780521890793
• ISBN: 978-0-521-89079-3
• Dimensions: 175 mm x 246 mm x 25 mm
• Subjects: Natural sciences, medicine, IT, technology; Social sciences, law, business > Social sciences (general); SCIENCE / Life Sciences / Neuroscience, Neurosciences, COMPUTERS / Data Science / Neural Networks, Neural networks and fuzzy systems, Neural networks & fuzzy systems