elephant.statistics.cv2

elephant.statistics.cv2(time_intervals, with_nan=False)[source]

Calculate the measure of Cv2 for a sequence of time intervals between events.

Given a vector I containing a sequence of intervals, the Cv2 is defined as:

Cv2 := \frac{1}{N} \sum_{i=1}^{N-1} \frac{2|I_{i+1}-I_i|} {|I_{i+1}+I_i|}

The Cv2 is typically computed as a substitute for the classical coefficient of variation (Cv) for sequences of events which include some (relatively slow) rate fluctuation. As with the Cv, Cv2=1 for a sequence of intervals generated by a Poisson process.

Parameters:
time_intervalspq.Quantity or np.ndarray or list

Vector of consecutive time intervals.

with_nanbool, optional

If True, cv2 of a spike train with less than two spikes results in a np.NaN value and a warning is raised. If False, ValueError exception is raised with a spike train with less than two spikes. Default: True.
Returns:
float

The Cv2 of the inter-spike interval of the input sequence.
Raises:
ValueError

If an empty list is specified, or if the sequence has less than two entries and with_nan is False.

If a matrix is passed to the function. Only vector inputs are supported.
Warns:
UserWarning

If with_nan is True and cv2 is calculated for a sequence with less than two entries, generating a np.NaN.

References

[1]G. R. Holt, W. R. Softky, C. Koch, & R. J. Douglas, “Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons,” Journal of Neurophysiology, vol. 75, no. 5, pp. 1806-1814, 1996.