For binary (spike train) signals, take FFT of signal or autocorrelation of signal? I want to characterize a binary time-series signal x (derived from neuron action potential data) in the frequency domain. 

Should I use the FFT of the original signal x, or the power spectrum (FFT of the autocorrelation of x, which is defined as $k(n)=E[x(t)\cdot x(t-n)]$ )?

Also, does anyone know any literature which explicitly talks about this, or at least took sides?
For binary data (spike-trains), people use a spike-train autocovariance function (same as autocorrelation but subtracting the mean firing rate). It's commonly used to study brain oscillations (e.g. Engel &amp; Konig, 1991). There is a good description of the math in the Dayan &amp; Abbott (page 27).

Dayan, P., &amp; Abbott, L. F. (2001). Theoretical neuroscience (Vol. 806). Cambridge, MA: MIT Press.
Engel, A. K., &amp; Konig, P. (1991). Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex. Science, 252(5009), 1177.