Adaptive learning in networks of spiking neurons for recognising patterns that change with time

尖峰神经元网络中的自适应学习，用于识别随时间变化的模式

• 批准号: DP0453205
• 负责人: Prof Anthony Burkitt
• 金额: $ 10.45万
• 依托单位: Bionics Institute
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2004
• 资助国家: 澳大利亚
• 起止时间: 2004-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0453205
• 关键词: Adaptive; learning; networks; spiking; neurons

The aim of this project is to develop a method for recognising patterns that change with time. Building-blocks similar to those in the brain (spiking neurons) will be used. Automatic techniques will be developed to teach groups of spiking neurons the differences between sequences of events by adjusting connections between neurons. The significance of this approach is that it captures information about timing that is missed in existing techniques. The development of a reliable method that is fast and robust to noise will have wide application in many areas, especially computer speech recognition where timing plays a crucial role.

该项目的目的是开发一种识别随时间变化的模式的方法。将使用类似于大脑中的构建块（尖峰神经元）。将开发自动技术，通过调整神经元之间的连接来教导尖峰神经元组事件序列之间的差异。这种方法的重要性在于，它捕获了现有技术中遗漏的有关时序的信息。发展一种可靠的方法，是快速和鲁棒的噪声将有广泛的应用在许多领域，特别是计算机语音识别中的时间起着至关重要的作用。