论文标题
尖峰拟态神经网络中的深度学习
Deep Learning in Spiking Phasor Neural Networks
论文作者
论文摘要
尖峰神经网络（SNN）引起了深度学习社区的注意，用于低延迟，低功率神经形态硬件以及了解神经科学的模型。在本文中，我们介绍了尖峰的相相神经网络（SPNNS）。 SPNN基于复杂值的深神经网络（DNN），代表峰值时间。我们的模型可以使用复杂的域来使用尖峰计时代码和梯度来计算强大的计算。我们在CIFAR-10上训练SPNN，并证明性能超过了其他计时编码的SNN，并以可比的实价DNN接近结果。
Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware, as well as models for understanding neuroscience. In this paper, we introduce Spiking Phasor Neural Networks (SPNNs). SPNNs are based on complex-valued Deep Neural Networks (DNNs), representing phases by spike times. Our model computes robustly employing a spike timing code and gradients can be formed using the complex domain. We train SPNNs on CIFAR-10, and demonstrate that the performance exceeds that of other timing coded SNNs, approaching results with comparable real-valued DNNs.
