Segmentation and Recognition of Complex Temporal Patterns

时间信息处理是包括听觉和视觉在内的各种智能行为的基础。提出了一种用于复杂时间模式分割和识别的神经网络框架。时间分段的处理基于这样的思想，即分段通过每个分段内的同步和不同分段之间的去同步来表示。每个线段成为识别网络的输入，识别网络显式地编码输入的局部特征的邻域或拓扑关系，并且识别基于图匹配方法。为了应对嵌入时间的问题，网络对时间进行了显式编码。多个时间模式被分成在时间域中交替激活的不同段。该网络能够识别复杂的时间模式，并且识别对于时间间隔的扭曲(时间扭曲)和呈现速率的变化是不变的。该网络将接受神经可信度和计算效率的测试。该项目的结果将提供新的计算原理，大脑可能会使用这些原理来处理时间分割和识别。此外，它们还将为解决实时连续审计师模式识别中不可或缺的技术问题提供有效的方法。

Temporal information processing underlies various kinds of intelligent behaviors, including hearing and vision. A neural network framework for segmenting and recognizing complex temporal patterns is proposed. Processing of temporal segmentation is based on the idea that segmentation is expressed by synchronization within each segment and desychronization among different segments. Each segment becomes an input to the recognition network that explicitly encodes neighborhood or topological relations of local features of the input, and recognition is based on the graph matching method. To cope with problems embedded in time, the network to constructed codes time explicitly. Multiple temporal patterns are segregated into different segments that are activated alternately in the time domain. The network is able to recognize complex temporal patterns, and recognition is invariant to distortions of time intervals (time warping) and to changes in the rate of presentation . The network will be tested for both neural plausibility and computational effectiveness. Results of this project will provide new computational principles that might be used by the brain to process temporal segmentation and recognition. Also, they will provide effective methods for solving technical problems indispensable in real time continuous auditor pattern recognition.