Single neuron firing is often analyzed relative to an external event, such as successful task performance, or the delivery of a stimulus. The peri-event time histogram (PETH) examines how - on average - neural firing modulates before and after the alignment event. However, the PETH contains no information about the single-trial reliability of the neural response, which is important from the point of view of a target neuron. In this paper, we propose the concept of using the neural activity to predict the timing of the occurrence of an event, as opposed to using the event to predict the neural response. We firstly estimate the likelihood of an observed spike train, under the assumption that it was generated by an inhomogeneous gamma process with rate profile similar to the PETH shifted by a small time. This is used to generate a probability distribution of the event occurrence, using Bayes' rule. By an information theoretic approach, this method yields a single value (in bits) which quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train. We show that the approach is sensitive to the amplitude of a response, to the level of baseline firing, and to the consistency of a response between trials, all of which are factors that will impact a neuron's ability to code for the time of the event. The technique can provide a useful means of determining which of several behavioral events a cell encodes best, as well as permitting objective comparison of different cell populations.