The Formation of Topographic Maps That Maximize the Average Mutual Information of the Output Responses to Noiseless Input Signals
The Formation of Topographic Maps That Maximize the Average Mutual Information of the Output Responses to Noiseless Input Signals
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最大化无噪声输入信号输出响应的平均互信息的地形图的形成
DOI：
10.1162/neco.1997.9.3.595
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发表时间：
1997
影响因子：
2.9
通讯作者：
M. V. Hulle
中科院分区：
文献类型：
--
作者：
M. V. Hulle
本文介绍了一个非常简单和局部的学习规则，以地形图的形成。该规则被称为最大熵学习规则（MER），对于任何类型的输入分布，最大化映射输出的无条件熵。本文的目的是表明，MER是一种可行的策略，用于建立地形图，最大限度地提高平均互信息的输出响应无噪声输入信号时，只有输入噪声和噪声添加的输入信号。
This article introduces an extremely simple and local learning rule for to pographic map formation. The rule, called the maximum entropy learning rule (MER), maximizes the unconditional entropy of the map's output for any type of input distribution. The aim of this article is to show that MER is a viable strategy for building topographic maps that maximize the average mutual information of the output responses to noiseless input signals when only input noise and noise-added input signals are available.