2010 Special Issue: Visualization of multi-neuron activity by simultaneous optimization of clustering and dimension reduction

Authors:
Narihisa Matsumoto;Shotaro Akaho;Yasuko Sugase-Miyamoto;Masato Okada
Affiliations:
Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba-shi, Ibaraki 305-8568, Japan;Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba-shi, Ibaraki 305-8568, Japan;Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, 1-1-1 Umezono, Tsukuba-shi, Ibaraki 305-8568, Japan;Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan and RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
Venue:
Neural Networks
Year:
2010

Citing 4
Cited 0

Mixtures of probabilistic principal component analyzers

Neural Computation
Independent factor analysis

Neural Computation
Adaptive dimension reduction for clustering high dimensional data

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Mixed Factors Model for Dimension Reduction and Extraction of a Group Structure in Gene Expression Data

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.