DeNN通过精确利用脉冲时间信息，实现高效低耗的时序建模。
Delay Neural Networks (DeNN) for exploiting temporal information in event-based datasets
- 基于输入突触延迟和脉冲时间计算神经元信息
- 在事件数据上表现优异，参数与能耗显著降低
- 适合对时序敏感的视觉/音频任务，如动态事件感知
在深度神经网络（DNN）和脉冲神经网络（SNN）中，神经元的信息基于接收电位幅度（权重）之和计算。本文提出一类新型神经网络——延迟神经网络（DeNN），其神经元信息基于输入突触延迟之和以及来自其他神经元的脉冲时间计算。该设计使DeNN在前向与反向传播中均能显式利用连续精确的脉冲时序信息，无需近似。实验表明，(深) DeNN应用于图像及事件基数据集（音频与视觉），在时序信息重要的任务中取得优异性能，且参数量和能耗远低于其他模型。
原文摘要 · Abstract (English)
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.
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