在 Spiking 神经网络中重新考虑跳过连接,使用 Time-To-First-Spike 编码
Youngeun Kim1, Adar Kahana2, Ruokai Yin1
1Department of Electrical Engineering, Yale University, New Haven, CT, United States.
Frontiers in neuroscience
|February 29, 2024
概括
在尖端神经网络 (SNN) 中使用时间到第一个尖端 (TTFS) 编码跳过连接可以提高性能. 在基于连接的跳过连接中,一种新的可学习的延迟增强了信息混合,以获得更好的结果.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 尖端神经网络 (SNN) 通过使用时间到第一个尖端 (TTFS) 编码模仿生物神经元来提供能源效率.
- 跳过连接在人工神经网络 (ANN) 中至关重要,但它们在TTFS编码的SNN中的作用需要调查.
研究的目的:
- 分析基于加法和连接的跳过连接对TTFS编码的SNN的影响.
- 提出一种新的方法来改善TTFS SNNs的连接式跳过连接中的信息混合.
主要方法:
- 在TTFS SNNs中研究了基于加法和基于连接的跳过连接架构.
- 引入了基于连接的跳过连接的可学习延迟机制.
- 在MNIST和时尚-MNIST数据集上进行实验.
主要成果:
- 基于添加的跳过连接引入了尖峰时间延迟.
- 基于并列的跳过连接会造成时间差距,阻碍信息混合.
- 建议的可学习延迟有效地弥合了时间差距,改善了信息混合.
结论:
- 跳过连接对于TTFS编码的SNNs来说可能是有益的,在仔细的架构设计的情况下.
- 可学习延迟方法提高了基于连接的跳过连接的有效性.
- 在图像识别之外,TTFS编码和跳过连接显示出有希望,包括科学机器学习.
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