基于时间约束Epperiment的神经网络连通性推断
Inferring Neuronal Network Connectivity using Time-constrained Episodes
摘要：
发现事件序列中的频繁片段是一项有趣的数据采掘任务。在本文中，我们认为，这个框架是非常有效的分析多神经元的棘波训练数据。分析尖峰训练数据是神经科学中的一个重要问题，尽管没有数据采掘方法报道。出于这个应用程序，我们引入不同的时间限制的事件发生。我们提出的算法，发现频繁发作的时间限制。通过模拟，我们表明，我们的方法是非常有效的分析尖峰训练数据挖掘底层的连接模式。
英文摘要：
Discovering frequent episodes in event sequences is an interesting data mining task. In this paper, we argue that this framework is very effective for analyzing multi-neuronal spike train data. Analyzing spike train data is an important problem in neuroscience though there are no data mining approaches reported for this. Motivated by this application, we introduce different temporal constraints on the occurrences of episodes. We present algorithms for discovering frequent episodes under temporal constraints. Through simulations, we show that our method is very effective for analyzing spike train data for unearthing underlying connectivity patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
