While deep learning has achieved remarkable success, there is no clear explanation about why it works so well. In order to discuss this question quantitatively, we need a mathematical framework that explains what learning is in the first place. After several considerations, we succeeded in constructing a mathematical framework that can provide a unified understanding of all types of learning, including deep learning and learning in the brain. We call it learning principle, and it follows that all learning is equivalent to estimating the probability of input data. We not only derived this principle, but also mentioned its application to actual machine learning models. For example, we found that conventional supervised learning is equivalent to estimating conditional probabilities, and succeeded in making supervised learning more effective and generalized. We also proposed a new method of defining the values of estimated probability using differentiation, and showed that unsupervised learning can be performed on arbitrary dataset without any prior knowledge. Namely, this method is a general-purpose machine learning in the true sense. Moreover, we succeeded in describing the learning mechanism in the brain by considering the time evolution of a fully or partially connected model and applying this new method. The learning principle provides solutions to many unsolved problems in deep learning and cognitive neuroscience.

深度学习的成功没有明确的解释，因此我们构建了一个数学框架来解释什么是学习。我们提出了学习原理，它能够统一理解所有类型的学习，包括神经网络和大脑中的学习。我们成功地将这一原理应用于实际的机器学习模型，并提出了一种新的估计概率值的方法，使得无监督学习可以在任意数据集上进行。此外，我们通过考虑完全或部分连接模型的时间演化以及应用这种新方法成功地描述了大脑中的学习机制。学习原理提供了深度学习和认知神经科学中许多未解决问题的解决方案。

大脑学习机制的学习原则和数学实现