Adaptive learning of spatiotemporal patterns: Development of multi-layer spiking neuron networks using Hebbian and competitive learning.

时空模式的自适应学习：利用Hebbian和竞争学习开发多层尖峰神经元网络。该项目的目的是开发一种识别随时间变化的模式的方法。发展一种可靠的、快速的、抗噪声的方法将在许多领域有广泛的应用，特别是在时间起着至关重要作用的计算机语音识别中。将使用与大脑类似的构建模块（尖峰神经元）。自动技术将用于通过调整事件序列之间的连接来教导一组尖峰神经元之间的差异。这种方法的重要之处在于，它捕获了现有技术中遗漏的有关时间的信息。

Adaptive learning of spatiotemporal patterns: Development of multi-layer spiking neuron networks using Hebbian and competitive learning. The aim of this project is to develop a method for recognising patterns that change in time. 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. Building-blocks similar to those in the brain (spiking neurons) will be used. Automatic techniques will be used to teach groups of spiking neurons the differences between sequences of events by adjusting connections between them. The significance of this approach is that it captures information about timing that is missed in existing techniques.