Note on the spectral analysis of neural spike trains

The links between the estimation of the spectral density of a train of δ functions, the estimation of the spectral density of a point process and that of a discrete random signal are established. It shows in particular that it is possible to reduce the estimation of the spectral density of a neural spike train to that of a regularly sampled 0–1 signal with consequent computational advantages. In addition, a technique involving decimation for speeding up the estimation of the spectral density without much additional error is proposed.