Synopses & Reviews
This is an introduction to spiking neurons for advanced undergraduate or graduate students.
Review
'The treatment undoubtedly holds pointers to future developments that will allow robots to come closer to their biological prototypes.' Journal of Robotica
Synopsis
This introduction to spiking neurons is aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; but it will also be useful for biologists who are interested in mathematical modelling. A large number of worked examples are embedded in the text, which is profusely illustrated. A comprehensive bibliography is provided. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.
Synopsis
Includes bibliographical references (p. 455-475) and index.
Synopsis
advanced undergraduate or graduate students.
Table 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.