Representing spike trains using constant sampling intervals

Sensory neurons encode external information by a series of times of action potentials, which is called a spike train. However, since it is a point process, it is hard to analyze. Here we propose a method for converting a spike train into a real-valued time series with a fixed sampling interval under the assumption of temporal codes. The proposed method yields time series that represent encoded signals. Especially when the method is applied to spike trains generated using integrate-and-fire models, the yielded time series look very similar to those of encoded information. The method works robustly even when a spike train is contaminated with noise. Since unlike filters it does not use its original signals for the conversion, the proposed method can be widely used for investigating spike train data in the real world.