通过CNN-UM分析多维神经活动

Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications Pub Date : 2002-07-22 DOI:10.1109/CNNA.2002.1035057

V. Gál, S. Grun, R. Tetzlaff

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引用次数: 2

摘要

本文证明了CNN-UM是分析多维二进制信号时间序列的一个很好的工具。开发的算法致力于处理电生理多神经元记录:我们的目标是找到特定的多维活动模式，这可能反映更高阶的功能细胞组装。分析包括两个部分:首先统计不同模式的出现次数，然后分别计算每个出现频率的统计显著性。

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Analyzing multidimensional neural activity via CNN-UM

In this paper we show that CNN-UM is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: the occurrences of different patterns are first counted, then the statistical significance of each occurrence frequency is calculated separately.

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Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications

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