The possibility of temporal coding in neural data through patterns of precise spike times has long been of interest in neuroscience. Recent and rapid advancements in experimental neuroscience make it not only possible, but also routine, to record the spikes of hundreds to thousands of cells simultaneously. These increasingly common large-scale data sets provide new opportunities to discover temporally precise and behaviourally relevant patterns of spiking activity across large populations of cells. At the same time, the exponential growth in size and complexity of new data sets presents its own methodological challenges. Specifically, it remains unclear how best to (1) discover precise spike-time coordination in data sets that challenge existing analysis techniques, and (2) determine whether detected coordination is relevant to behaviour. Here, we introduce a new approach for analyzing the structure of spike-time coordination, in which patterns of spikes are represented as complex-valued vectors. This approach discovers clusters of similar spike patterns, makes effective links between spike timing and behaviour, and provides insight into the structure of putative spike-time codes
