尖刺神经网络 [英] Spiking neural networks

问题描述

在尖刺式神经网络领域，应该从哪本书开始?我知道Gerstner的加标神经元模型" ，出版于2002年.是一本较新的书，还是一本更合适的书?我有数学和人工神经网络的背景.

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.

如果此领域中有一些不错的文章或概述，请将它们也添加到列表中.

If there are some good articles or overviews in this domain, also add them to the list.

谢谢.

最新编辑

卡雷尔的答案:

"这取决于尖刺神经网络的意思-有 至少有几个基本观点. Gerstner代表第一个 一个-他专注于生物神经元的建模.还有他的书 从2002年开始，对于理解生物物理确实是一个很好的起点 神经元模型.过去也有可能找到这本书 在html中.

" 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 ..

另一方面，在计算机科学领域通过向神经元加标" 通常是指SRMo模型(峰值响应模型)，可以是 还用作基于Percepron的经典网络的替代方法.

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.

在沃尔夫冈·马斯(Wolfgang Maass)的作品中很好地描述了这种模型 ( http://www.igi.tugraz.at/maass/).他专注于计算 模型的强大功能，他将SRM模型与percepron和 RBF单位.

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.

如果您想在网络中使用该模型，我建议您使用 派生SpikeProp的Sander Bohte( http://homepages.cwi.nl/~sbohte/) 算法.

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.

(我本人派生了SpikeProp的一个变体，它足够快，可以 用于实词应用.)"

(I personally derived a variant of SpikeProp which was fast enough to be used for real-word applications.) "

推荐答案

我想建议两本书:

• 托马斯·特拉彭伯格(Thomas Trappenberg)的计算神经科学基础知识
• 理论神经科学:彼得·达扬(Peter Dayan)对神经系统的计算和数学建模

我个人派生了一种远程监督方法(ReSuMe)的变体，与Filip Ponulak推出的ReSuMe相比，它具有更好的学习率和形态学优势.

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.

同时，我想列出一些处理SNN的模拟器工具.我玩过的大多数游戏都是基于Python的，因此也请考虑到这一点.可能还有其他基于其他语言的其他语言.

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
• Nengo
• 大象
• 神经元
• PyNN
• 巢
• PyNest
• PCSIM
• Pypcsim

• ANNarchy
• Brian2
• Nengo
• Elephant
• Neuron
• PyNN
• Nest
• PyNest
• PCSIM
• Pypcsim

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