论文标题
开发用于尖峰神经网络的模型
Models Developed for Spiking Neural Networks
论文作者
论文摘要
深度神经网络（DNNS）的出现再次引起了人们对人工神经网络（ANN）的极大关注。它们已成为最先进的模型，并赢得了不同的机器学习挑战。尽管这些网络受到大脑的启发，但它们缺乏生物学上的合理性，与大脑相比，它们具有结构上的差异。尖峰神经网络（SNN）已经存在了很长时间，并且已经进行了研究以了解大脑的动力学。但是，它们在现实世界和复杂的机器学习任务中的应用是有限的。最近，他们在解决此类任务方面表现出巨大的潜力。由于它们的能源效率和时间动态，他们的未来发展有许多承诺。在这项工作中，我们回顾了SNN在图像分类任务上的结构和性能。比较表明，这些网络在更复杂的问题上显示出很大的功能。此外，为SNN制定的简单学习规则（例如STDP和R-STDP）可能是替代DNN中使用的反向传播算法的潜在替代方法。
Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared to the brain. Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain. However, their application in real-world and complicated machine learning tasks were limited. Recently, they have shown great potential in solving such tasks. Due to their energy efficiency and temporal dynamics there are many promises in their future development. In this work, we reviewed the structures and performances of SNNs on image classification tasks. The comparisons illustrate that these networks show great capabilities for more complicated problems. Furthermore, the simple learning rules developed for SNNs, such as STDP and R-STDP, can be a potential alternative to replace the backpropagation algorithm used in DNNs.
