Analysis of spike train data: An application of $k$-mean alignment

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.