Unsupervised learning and self-organization in networks of spiking neurons
Unsupervised learning and self-organization in networks of spiking neurons
复制标题
尖峰神经元网络中的无监督学习和自组织
DOI：
10.1007/978-3-7908-1810-9_3
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
M. Schmitt
中科院分区：
文献类型：
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作者：
Thomas Natschlüer;Berthold Ruf;M. Schmitt
生物神经系统最突出的特征之一是单个神经元通过短电脉冲进行通信，即所谓的动作电位或尖峰。在本章中，我们研究了无监督学习和自组织在尖峰神经元网络中的可能机制。在简要介绍了脉冲神经元网络之后，我们描述了一种在高维输入空间或其子空间中寻找簇的生物学上合理的算法，该算法即使在动态变化的环境中也能工作。此外，我们研究的尖峰神经元的自组织地图显示，网络的尖峰神经元使用时间编码可以实现一个拓扑保持行为非常类似的Kohonen的自组织地图。对于这些网络的竞争性计算的机制，提出了基于动作电位时序。因此，竞争神经元群体中的赢家可以在本地确定，并且通常比使用速率编码的方法更快。本章介绍的模型和算法为生物神经系统中无监督学习的更真实描述奠定了基础。
One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so-called action potentials or spikes. In this chapter we investigate possible mechanisms of unsupervised learning and self-organization in networks of spiking neurons. After giving a brief introduction to spiking neuron networks we describe a biologically plausible algorithm for these networks to find clusters in a high dimensional input space or a subspace of it. The algorithm is shown to work even in a dynamically changing environment. Futhermore, we study self-organizing maps of spiking neurons showing that networks of spiking neurons using temporal coding can achieve a topology preserving behavior quite similar to that of Kohonen's self-organizing map. For these networks a mechanism of competitive computation is proposed that is based on action potential timing. Thus, the winner in a population of competing neurons can be determined locally and in generally faster than in approaches which use rate coding. The models and algorithms presented in this chapter establish further steps toward more realistic descriptions of unsupervised learning in biological neural systems.