Many of the techniques used to investigate these coding strategies require the neural activity to be tightly linked to particular stimulus attributes. However, in the case of cognitive processes such as decision making or planning, the temporal correlation between neural activity and stimulus attributes may vary considerably from trial to trial. We have developed a method that can potentially identify salient features in neural population recordings regardless of when those features occur. The model can also suggest when during a trial a decision was made. We apply this model to population recordings of rats doing a simple maze exploration task, and compare the results to standard pattern recognition approaches.