Rezghi Shirsavar Shahriar, Vahabie Abdol-Hossein, A Dehaqani Mohammad-Reza
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
MethodsX. 2023 Mar 24;10:102157. doi: 10.1016/j.mex.2023.102157. eCollection 2023.
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.•Different building blocks of spiking neural networks are explained in this work.•Developed models for SNNs are introduced based on their characteristics and building blocks.
深度神经网络（DNN）的出现再次引发了对人工神经网络（ANN）的极大关注。它们已成为最先进的模型，并在各种机器学习挑战中获胜。尽管这些网络的灵感来源于大脑，但它们缺乏生物学上的合理性，并且与大脑相比存在结构差异。脉冲神经网络（SNN）已经存在了很长时间，人们对其进行了研究以了解大脑的动态。然而，它们在现实世界和复杂机器学习任务中的应用受到限制。最近，它们在解决此类任务方面显示出巨大潜力。由于其能源效率和时间动态特性，它们在未来发展中有许多前景。在这项工作中，我们回顾了SNN在图像分类任务中的结构和性能。比较表明，这些网络在处理更复杂问题时表现出强大的能力。此外，为SNN开发的简单学习规则，如STDP和R-STDP，可能是替代DNN中使用的反向传播算法的潜在选择。
• 本文解释了脉冲神经网络的不同构建模块。
• 根据其特征和构建模块介绍了为SNN开发的模型。
