Analysis of Multineuron Activity Using the Kernel Method
Masaki Nomura*, Yoshio Sakurai*,**, and Toshio Aoyagi*,***
*CREST, Japan Science and Technology Corporation, Kawaguchi, Saitama 332-0012, Japan
**Department of Psychology, Graduate School of Letters, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
***Department of Applied Analysis and Complex Dynamical Systems, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
We recorded multineuron spike time-series data from rat hippocampus region CA1 during a conditional discrimination task. We separated out individual single-neuron activity from multineuron activity data and prepared spike count data and calculated a kernel matrix using a Spikernel function, then applied k-means clustering and principal component analysis (PCA). Comparing spike count data to an appropriate time, we divided data into clusters and found the correspondence between the obtained cluster and rat activity. We discuss information expression in nervous-system activity expected from the kernel function.
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