导致无监督学习的神经网络理论

H. Szu
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引用次数: 1

摘要

在本文中,我们推导了一个精确的单像素BSS解决方案。进一步,我们利用增广Lagrange或Karush, Kuhn和Tucker方法证明了n个分量的解是唯一和稳定的[S 07]。我们的恒温自由能可以估计大脑灰质的神经元数量,这些神经元负责克里克和科赫所确定的意识活动,即在放电率之间完成绑定的闭孔体(类似于管弦乐队演奏开始时的c节点调谐)。此外,视网膜神经元反应墨西哥帽功能可以解释为有限资源共享补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Theories of Neural Networks Leading to Unsupervised Learning
In this paper, we derive an exact single-pixel BSS solution for two components. Furthermore, we prove the solution for n components to be unique and stable by means of the augmented Lagrange or Karush, Kuhn and Tucker methodology [S 07]. Our constant-temperature free energy can estimate the neuronal population of brain's grey matter which is responsible for the consciousness activities identified by Crick & Koch as the Claustrum accomplishing binding among firing rates (similar to C-node tuning in the beginning of an orchestra performance). Furthermore, the retinal neuronal response Mexican hat functions could be explained by finite resource sharing for replenishment.
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