Temporal Hebbian Self-Organizing Map for Sequences
Temporal Hebbian Self-Organizing Map for Sequences
复制标题
序列的时态赫布自组织映射
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
M. Snorek
中科院分区：
文献类型：
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作者：
J. Koutník;M. Snorek
本文提出了一种新的自组织神经网络，称为时间序列自组织映射（THSOM），适用于时间序列的建模。该网络基于Kohonen的自组织映射，该映射在神经元之间扩展了一层完整的循环连接。循环连接层用Hebb规则进行训练。循环层表示输入向量的时间顺序。该算法利用欧几里得度量和标量积神经元将上下文信息直接嵌入到循环SOM中。循环层可以很容易地转换为随机自动机（马尔可夫链）生成序列，用于之前的THSOM训练。最后，给出了两个实际使用THSOM的例子。将THSOM应用于从GPS数据中提取道路网络，并构建人体内测量的刺突序列时空模型。
In this paper we present a new self-organizing neural network called Temporal Hebbian Self-organizing Map (THSOM) suitable for modelling of temporal sequences. The network is based on Kohonen's Self-organizing Map, which is extended with a layer of full recurrent connections among the neurons. The layer of recurrent connections is trained with Hebb's rule. The recurrent layer represents temporal order of the input vectors. The THSOM brings a straightforward way of embedding context information in recurrent SOM using neurons with Euclidean metric and scalar product. The recurrent layer can be easily converted into a stochastic automaton (Markov Chain) generating sequences used for previous THSOM training. Finally, two real world examples of THSOM usage are presented. THSOM was applied to extraction of road network from GPS data and to construction of spatio-temporal models of spike train sequences measured in human brain in vivo.