Statistical method for detection of firing rate changes in spontaneously active neurons

We present a simple statistical method for detection and identification of firing rate changes in spontaneously active neurons. Spontaneously active neurons (such as olfactory neurons) can be, in response to stimulation, either excited or suppressed and thus increase or decrease the spike firing rate. The described method is based on the detection of changes in slope of cumulative spike time distribution and efficiently detects excitations and suppressions. Using the simulated spike trains we examined the method's power in relation to response strength and duration.