论文标题
由Lyapunov函数控制的网络动力学：从内存到分类
Network Dynamics Governed by Lyapunov Functions: From Memory to Classification
论文作者
论文摘要
1982年，约翰·霍普菲尔德（John Hopfield）发表了一种用于记忆检索的神经网络模型，该模型成为理论神经科学中的基石。 Hopfield模型的关键要素是使用由Lyapunov函数控制的网络动力学。在最近的一篇论文中，Krotov和Hopfield展示了Lyapunov功能如何控制神经网络连通性的生物学合理学习规则。通过这样做，他们为分类任务带来了一种有趣的方法，并在该领域数十年来展示了更广泛的框架的相关性。
In 1982 John Hopfield published a neural network model for memory retrieval, a model that became a cornerstone in theoretical neuroscience. A key ingredient of the Hopfield model was the use of a network dynamics that is governed by a Lyapunov function. In a recent paper, Krotov and Hopfield showed how a Lyapunov function governs a biological plausible learning rule for the neural networks' connectivity. By doing so, they bring an intriguing approach to classification tasks, and show the relevance of the broader framework across decades in the field.
