It was once common to believe that the timing of spikes in spike trains was too variable to carry information and that in the sensory pathways and that information about stimuli was represented by spike rates. While it is now thought possible that stimuli are also represented in spike-timing features, it is still not fully understood how to describe the variability and coding function of these features. One approach is to define a spike train metric, that is, a measure of the distance between two spike trains. A good metric will measure a short distance between responses to the same input and a longer distance between responses to different inputs and can be used to quantify the significance of variability between putative timing features. Here, we define a new metric. It is constructed using a non-linear transformation of spike trains into functions and is motivated physiologically by a simple model of synaptic conductance which takes adaptation into account. This metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the field L auditory area of the zebra finch fore-brain.