Sparse Data Analysis Strategy for Neural Spike Classification

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="757068.fig.001a" src="https://static.hindawi.com/articles/cin/volume-2014/757068/figures/757068.fig.001a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Matrix of initial relations</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="757068.fig.001b" src="https://static.hindawi.com/articles/cin/volume-2014/757068/figures/757068.fig.001b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Seriated matrix</td></tr></table></td></tr><tr class="fig-image" id="c"><td><img alt="757068.fig.001c" src="https://static.hindawi.com/articles/cin/volume-2014/757068/figures/757068.fig.001c.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(c) </b>Pixelized representation of the ordered matrix</td></tr></table></td></tr></table>

<table>Effects of an algorithm of arrangement on a data set.</table>

Computational Intelligence and Neuroscience

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Figure 1

Figure 1: Sparse Data Analysis Strategy for Neural Spike Classification 