A Cerebral Cortex Model that Self-Organizes Conditional Probability Tables and Executes Belief Propagation
A Cerebral Cortex Model that Self-Organizes Conditional Probability Tables and Executes Belief Propagation
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自组织条件概率表并执行置信传播的大脑皮层模型
DOI：
10.1109/ijcnn.2007.4370951
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Yuuji Ichisugi
中科院分区：
文献类型：
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作者：
Yuuji Ichisugi
本文描述了一种大脑皮层的神经网络模型BESOM模型，它利用自组织映射获取贝叶斯网络的条件概率表，并利用近似置信传播算法估计随机变量的状态。近似算法是从一些假设推导出来的。一个神经网络，执行派生的算法是在良好的协议与六层和列结构，代表了大脑皮层的解剖特征，在许多方面。该模型具有可扩展的时间和空间复杂性，因此有资格成为大脑的模型，一个大规模的信息处理器。
This paper describes a neural network model of cerebral cortex, BESOM model, that acquires conditional probability tables for a Bayesian network using self-organizing maps and estimates states of random variables with an approximate belief propagation algorithm. The approximate algorithm is derived from some assumptions. A neural network that executes the derived algorithm is in good agreement with six-layer and column structures that represent the anatomical characteristics of a cerebral cortex in many respects. This model has scalable time and space complexities and is therefore qualified to be a model of the brain, a large-scale information processor.