Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 2, 1-1-1 Umezono, Tsukuba-shi, Ibaraki, Japan.
Neural Netw. 2010 Aug;23(6):743-51. doi: 10.1016/j.neunet.2010.05.003. Epub 2010 May 12.
The recent development of arrays of microelectrodes have enabled simultaneous recordings of the activities of more than 100 neurons. However, it is difficult to visualize activity patterns across many neurons and gain some intuition about issues such as whether the patterns are related to some functions, e.g. perceptual categories. To explore the issues, we used a variational Bayes algorithm to perform clustering and dimension reduction simultaneously. We employed both artificial data and real neuron data to examine the performance of our algorithm. We obtained better clustering results than in a subspace that were obtained by principal component analysis.
最近微电极阵列的发展使得同时记录 100 多个神经元的活动成为可能。然而，很难可视化许多神经元的活动模式，并获得一些关于模式是否与某些功能（例如感知类别）相关的直观认识。为了探讨这些问题，我们使用变分贝叶斯算法同时进行聚类和降维。我们使用人工数据和真实神经元数据来检查我们算法的性能。我们获得了比主成分分析获得的子空间更好的聚类结果。
