Segmentation and Recognition of Complex Temporal Patterns

时间信息处理是各种 智能行为，包括听觉和视觉。 神经 一种用于复杂时态分割和识别的网络框架 提出了模式。 时间分割的处理是基于 基于分段由同步表示的思想， 在每个段内以及在不同段之间的去同步。 每个片段都成为识别网络的输入， 显式编码局部的邻域或拓扑关系 输入的特征，识别是基于图 匹配法 为了科普时间中的问题， 网络的时间构造码。 多个时间 模式被分成不同的片段， 在时间域中交替地。 网络能够识别 复杂的时间模式，识别是不变的 时间间隔的扭曲（时间扭曲）和时间间隔的变化 介绍率。 该网络将测试两个神经 合理性和计算有效性。 成果 该项目将提供新的计算原理， 被大脑用来处理时间分割和识别。 同时，也为解决技术问题提供了有效的方法 真实的实时连续审计模式中不可或缺的问题 识别.

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.