论文标题
尖峰神经网络 - 第一部分：检测空间模式
Spiking Neural Networks -- Part I: Detecting Spatial Patterns
论文作者
论文摘要
尖峰神经网络（SNN）是具有生物学启发的机器学习模型，其基于动态神经元模型，以在事件驱动的在线，时尚中处理二进制和稀疏的尖峰信号。 SNN可以在神经形态计算平台上实施，这些平台正在成为用于学习和推理的节能协作者。这是三篇论文系列中的第一篇，通过专注于模型，算法和应用程序，将SNN介绍给了工程师的受众。在第一篇论文中，我们首先介绍用于常规人工神经网络（ANN）和SNN的神经模型。然后，我们回顾了旨在通过检测或生成速率编码的尖峰信号中的空间模式来模仿ANN功能的SNN的学习算法和应用程序。我们特别讨论了ANN-SNN转换和神经采样。最后，我们验证了SNN通过实验检测和生成空间模式的功能。
Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driven, online, fashion. SNNs can be implemented on neuromorphic computing platforms that are emerging as energy-efficient co-processors for learning and inference. This is the first of a series of three papers that introduce SNNs to an audience of engineers by focusing on models, algorithms, and applications. In this first paper, we first cover neural models used for conventional Artificial Neural Networks (ANNs) and SNNs. Then, we review learning algorithms and applications for SNNs that aim at mimicking the functionality of ANNs by detecting or generating spatial patterns in rate-encoded spiking signals. We specifically discuss ANN-to-SNN conversion and neural sampling. Finally, we validate the capabilities of SNNs for detecting and generating spatial patterns through experiments.
