Wulfram Gerstner, Werner M. Kistler
This is an introduction to spiking neurons for advanced undergraduate or graduate students. It can be used with 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. No prior knowledge beyond undergraduate mathematics is necessary to follow the book. Thus it should appeal to students or researchers in physics, mathematics, or computer science interested in biology; moreover it will also be useful for biologists working in mathematical modeling.
which is the book one should start with in the domain of spiking neural networks? I know about Gerstner's "Spiking Neuron Models", published in 2002. Is there a more recent book, or maybe a more suitable one? I have a background in maths and artificial neural networks.
If there are some good articles or overviews in this domain, also add them to the list.
LATER EDIT: Karel's answer follows: " It depends what do you mean by spiking neural networks - there are at least several basic points of view. Gerstner represents the first one - he is focused on modelling of biological neurons. And his book from 2002 is really good starting point for understanding bio-physical models of neuron. It the past it was possible to find this book also in html ..
On the other hand by ¨Spiking neuron" in the computer science context is usually meant the SRMo model (Spike Response Model), which can be used also as an alternative to classical percepron-based networks.
This model is described very well in the works of Wolfgang Maass (http://www.igi.tugraz.at/maass/). He has focused on the computational power of the model and he compares the SRM model with percepron and RBF-unit.
If you want to use the model in a network I recommend to you works of Sander Bohte (http://homepages.cwi.nl/~sbohte/) who derived SpikeProp algorithm.
(I personally derived a variant of SpikeProp which was fast enough to be used for real-word applications.) "