Question: Which is the book one should start with in the domain of spiking neural networks? I know about Gerstner's ["Spiking Neuron Models"](https://rads.stackoverflow.com/amzn/click/com/0521890799), 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. 

Thanks. 

**LATER EDIT**

Karel's answer:

> 
> " 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.) "
> 
> 
>

Answer: 
Spiking Neural Networks (SNNs) or Pulsed Neural Networks (PNNs) are artificial neural networks (ANNs) that more closely emulate the behaviours of natural neural mechanisms. I would like to advise you following fundamental books:

* Fundamentals of Computational Neuroscience by Thomas Trappenberg
* Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan
* Spiking Neuron Models: Single Neurons, Populations, Plasticity by Wulfram Gerstner

I personally derived a variant of Remote Supervised Method (ReSuMe) which has better learning rate and morphological advantages compared to ReSuMe introduced by Filip Ponulak.

In the meantime, I would like to list some of simulator tools dealing with SNNs. Most of them which I played with are based on Python so please take into account that as well. There might be more others based on other languages.

* ANNarchy
* [Brian2](https://brian2.readthedocs.io/en/stable/)
* Nengo
* [Elephant](https://elephant.readthedocs.io)
* Neuron
* [PyNN](https://neuralensemble.org/docs/PyNN/)
* [NEST / pynest](https://www.nest-simulator.org)
* PCSIM
* Pypcsim
* [BindsNet](https://github.com/BindsNET/bindsnet)
Don't forget [BindsNet](https://github.com/BindsNET/bindsnet): Best choice. And trending.