Abstract
We analyze the spike train data by means of the $k$-mean alignment algorithm in a double perspective: data as non periodic and data as periodic. In the first analysis, we show that alignment is not needed to identify paths. Indeed, without allowing for warping, we detect four clusters strongly associated to the four possible paths. In the second analysis, by exploiting the circular nature of data and allowing for shifts, we detect two clusters distinguishing between spike trains presenting higher or lower neuronal activity during the bottom-left/bottom-right movement respectively. In this latter case, the alignment procedure is able to match the four movements across paths.
Highlights
We here analyze the spike train data presented in Wu, Hatsopoulos and Srivastava (2014) with the aim of detecting spike trains associated to different paths or movements
This manuscript is divided in two sections: in the first one we analyze the 240 spike trains as functions defined on a common domain along the real axis; in the second section, given the circularity of the four possible paths, we analyze the 240 spike trains as periodic functions
Since the trajectories of the monkey right hand should be ideally close curves, always the same across paths, and with just the starting points differing across paths, we here analyze the 240 spike trains as periodic functions and apply the k-mean alignment with a similarity measure similar to that used in the previous section: ρ(fi, fj) =
Summary
We here analyze the spike train data presented in Wu, Hatsopoulos and Srivastava (2014) with the aim of detecting spike trains associated to different paths or movements. All analyses have been performed using the fdakma R package downloadable from CRAN (Parodi et al (2014))
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