Reconstruction of Visual Sensory Space on the Hidden Layer in Layered Neural Networks
Reconstruction of Visual Sensory Space on the Hidden Layer in Layered Neural Networks
复制标题
分层神经网络隐藏层视觉感觉空间的重构
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
Koji Ito
中科院分区：
文献类型：
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作者：
K. Shibata;Koji Ito
在分层神经网络中，通过输入层到隐层的连接权值和每个隐层神经元的输出函数，在隐层上重构输入空间。通过学习对连接权进行艾德修正，实现了强调必要信息、退化不必要信息的变换，以计算输出。本文采用视觉感官信号作为输入。为了检查重建，（1）在第一次将监督或强化学习应用于分层神经网络，（2）将从隐藏层到输出层的所有连接权重重置为0，（3）应用使用一些训练数据的另一监督学习，最后（4）将测试数据的输出与未应用第一次学习时的输出进行比较。结果表明，在第一次学习中产生所需输出的必要信息是在隐藏层上提取的。
In layered neural networks, the input space is reconstructed on the hidden layer through the connection weights from the input layer to the hidden layer and the output function of each hidden neuron. The connection weights are modi ed by learning and realize the transformation to emphasize necessary information and to degenerate unnecessary one for calculating the output. In this paper, visual sensory signals are adopted as the input. In order to examine the reconstruction, (1)supervised or reinforcement learning is applied to a layered neural network at rst, (2)all the connection weights from the hidden layer to the output layer are reset to 0, (3)another supervised learning using some training data is applied, and nally (4)the output for the test data is compared to that when the rst learning was not applied. It is shown that the necessary information to generate the desired output in the rst learning was extracted on the hidden layer.