标题： 基于事件的数据集中的时间信息利用的延迟神经网络（DeNN） 显示英文标题

标题： Delay Neural Networks (DeNN) for exploiting temporal information in event-based datasets

摘要： 在深度神经网络（DNN）和脉冲神经网络（SNN）中，神经元的信息是根据从其他神经元接收的电位幅度（权重）之和计算的。 我们在这里提出了一种新的神经网络类别，即延迟神经网络（DeNN），其中神经元的信息是根据其输入突触延迟的总和以及从其他神经元接收的电位的脉冲时间计算的。 这样，DeNN 被设计为在正向和反向传递中显式使用脉冲的精确连续时间信息，而无需近似。 （深度）DeNN 在这里被应用于图像和基于事件的（音频和视觉）数据集。 获得了良好的性能，特别是在时间信息重要的数据集上，参数和能耗都比其他模型少得多。 显示英文摘要

摘要： In Deep Neural Networks (DNN) and Spiking Neural Networks (SNN), the information of a neuron is computed based on the sum of the amplitudes (weights) of the electrical potentials received in input from other neurons. We propose here a new class of neural networks, namely Delay Neural Networks (DeNN), where the information of a neuron is computed based on the sum of its input synaptic delays and on the spike times of the electrical potentials received from other neurons. This way, DeNN are designed to explicitly use exact continuous temporal information of spikes in both forward and backward passes, without approximation. (Deep) DeNN are applied here to images and event-based (audio and visual) data sets. Good performances are obtained, especially for datasets where temporal information is important, with much less parameters and less energy than other models.