Self-organizing neural projections
Self-organizing neural projections
复制标题
DOI：
10.1016/j.neunet.2006.05.001
复制
发表时间：
2006-07-01
期刊：
影响因子：
7.8
通讯作者：
Kohonen, Teuvo
中科院分区：
文献类型：
--
作者：
Kohonen, Teuvo
自组织映射（SOM）算法是为了创建抽象特征映射而开发的。它已被广泛接受为一种数据挖掘工具，其原理也可以解释大脑的特征图是如何形成的。然而，将该算法用于逐点神经投影模型（例如躯体位置图或视野图）是不正确的，首先，因为SOM不传递信号模式：其输出端的赢家通吃函数只定义了一个奇异响应。原始SOM也不能对叠加的刺激模式产生叠加的反应。本报告介绍了一种新的自组织系统模型相关的SOM，具有线性传递函数的模式和模式的组合所有的时间。从一对随机互连的神经层开始，并使用随机混合的模式进行训练，它创建了从输入层到输出层的逐点有序投影。如果输入层由特征检测器组成，则输出层形成输入的特征图。(c)2006爱思唯尔有限公司保留所有权利。
The Self-Organizing Map (SOM) algorithm was developed for the creation of abstract-feature maps. It has been accepted widely as a data-mining tool, and the principle underlying it may also explain how the feature maps of the brain are formed. However, it is not correct to use this algorithm for a model of pointwise neural projections such as the somatotopic maps or the maps of the visual field, first of all, because the SOM does not transfer signal patterns: the winner-take-all function at its output only defines a singular response. Neither can the original SOM produce superimposed responses to superimposed stimulus patterns. This presentation introduces a new self-organizing system model related to the SOM that has a linear transfer function for patterns and combinations of patterns all the time. Starting from a randomly interconnected pair of neural layers, and using random mixtures of patterns for training, it creates a pointwise-ordered projection from the input layer to the output layer. If the input layer consists of feature detectors, the output layer forms a feature map of the inputs. (c) 2006 Elsevier Ltd. All rights reserved.