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.Read more
- Modern approach takes the student to current research
- No unnecessary advanced mathematics, so suited for a broad audience
- Lots of illustrations and examples
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- Date Published: August 2002
- format: Paperback
- isbn: 9780521890793
- length: 496 pages
- dimensions: 244 x 177 x 23 mm
- weight: 0.99kg
- availability: Available
Table of Contents
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
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