论文标题
使用单个神经元的深神经网络：使用反馈调制的延迟回路折叠时间架构
Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
论文作者
论文摘要
深度神经网络是应用最广泛的机器学习工具之一，在各种任务中显示出出色的性能。我们提出了一种将任意大小的深神经网络折叠成具有多个时间延迟反馈回路的单个神经元的方法。这个单神经元的深神经网络仅包含单个非线性，并适当调整了反馈信号的调制。该网络及时出现是神经元动力学的时间展开。通过调整循环中的反馈调制，我们可以调整网络的连接权重。这些连接权重通过后传播算法确定，在该算法中，必须考虑延迟引起的网络连接和本地网络连接。我们的方法可以完全代表标准的深神经网络（DNN），包括稀疏的DNN，并将DNN概念扩展到动态系统实现。我们称之为折叠时DNN（FIT-DNN）的新方法在一组基准任务中表现出令人鼓舞的性能。
Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron's dynamics. By adjusting the feedback-modulation within the loops, we adapt the network's connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.
