The present paper aims to propose an information-theoretic method for interpreting the inference mechanism of neural networks. The new method aims to interpret the inference mechanism minimally by disentangling complex information into simpler and easily interpretable information. This disentanglement of complex information can be realized by maximizing mutual information between input patterns and the corresponding neurons. However, because the use of mutual information has faced difficulty in computation, we use the well-known autoencoder to increase mutual information by re-interpreting the sparsity constraint, which is considered a device to increase mutual information. The computational procedures to increase mutual information are decomposed into the serial operation of equal use of neurons and specific responses to input patterns. The specific responses are realized by enhancing the results by the equal use of neurons. The method was applied to three data sets: the glass, office equipment, and pulsar data sets. With all three data sets, we could observe that, when the number of neurons was forced to increase, mutual information could be increased. Then, collective weights, or average collectively treated weights, showed that the method could extract the simple and linear relations between inputs and targets, making it possible to interpret the inference mechanism minimally.

本文旨在提出一种信息理论方法来解释神经网络的推理机制。该新方法旨在通过将复杂信息分解为更简单，易于解释的信息，从而最小化解释推理机制。复杂信息的这种纠缠可以通过最大化输入模式和相应神经元之间的互信息来实现。但是，由于互信息的使用面临计算上的困难，因此我们使用众所周知的自动编码器通过重新解释稀疏性约束来增加互信息，稀疏约束被认为是一种增加互信息的设备。增加相互信息的计算过程被分解为等效使用神经元和对输入模式的特定响应的串行操作。通过平等使用神经元来增强结果，从而实现特定反应。该方法已应用于三个数据集：玻璃，办公设备和脉冲星数据集。使用所有这三个数据集，我们可以观察到，当神经元数量被迫增加时，相互信息可能会增加。然后，集体权重或平均集体处理权重表明，该方法可以提取输入与目标之间的简单线性关系，从而可以最小化解释推理机制。相互信息可以增加。然后，集体权重或平均集体处理权重表明，该方法可以提取输入与目标之间的简单线性关系，从而可以最小化解释推理机制。相互信息可以增加。然后，集体权重或平均集体处理权重表明，该方法可以提取输入与目标之间的简单线性关系，从而可以最小化解释推理机制。