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Computing the template, or the mean, of a set of spike trains is a novel and important task in neural coding. Due to the random nature of spike trains taken from experimental recordings, probabilistic and statistical methods have gained prominence in examining underlying firing patterns. However, these methods focus on modeling neural activity at each given time and therefore their results depend heavily on model assumptions. Taking a model-free and metric-based approach, we analyze the spacedoi:10.1109/embc.2012.6346181 pmid:23366142 dblp:conf/embc/WuS12a fatcat:acbvlp4f7femxfgbnkeblqfxeu