论文标题
神经元相关：神经网络中的一个核心概念
Neuronal Correlation: a Central Concept in Neural Network
论文作者
论文摘要
本文提议通过神经元相关研究神经网络，这是对倒数第二层神经元活性的统计量度。我们表明，可以通过权重矩阵有效地估算神经元相关性，可以通过层结构有效地执行，并且是网络概括能力的有力指标。更重要的是，我们表明神经元相关性对高维隐藏空间中熵估计的准确性显着影响。尽管由于对神经元独立性的隐式假设，以前的估计方法可能会遭受明显的不准确性，但我们提出了一种新型的计算方法，可以考虑神经元相关性，以具有有效且真实的熵计算。在此过程中，我们将神经元相关性作为神经网络的中心概念。
This paper proposes to study neural networks through neuronal correlation, a statistical measure of correlated neuronal activity on the penultimate layer. We show that neuronal correlation can be efficiently estimated via weight matrix, can be effectively enforced through layer structure, and is a strong indicator of generalisation ability of the network. More importantly, we show that neuronal correlation significantly impacts on the accuracy of entropy estimation in high-dimensional hidden spaces. While previous estimation methods may be subject to significant inaccuracy due to implicit assumption on neuronal independence, we present a novel computational method to have an efficient and authentic computation of entropy, by taking into consideration the neuronal correlation. In doing so, we install neuronal correlation as a central concept of neural network.
