CuBIC is a statistical method for the detection of higher order of correlations in parallel spike trains based on the analysis of the cumulants of the population count. Given a list sts of SpikeTrains, the analysis comprises the following steps:
>>> binsize = 5 * pq.ms
>>> pop_count = elephant.statistics.time_histogram(sts, binsize)
>>> alpha = 0.05 # significance level of the tests used
>>> xi, p_val, k = cubic(data, ximax=100, alpha=0.05, errorval=4.):
elephant.cubic.
cubic
(data, ximax=100, alpha=0.05)[source]¶Performs the CuBIC analysis [1] on a population histogram, calculated from a population of spiking neurons.
The null hypothesis is iteratively
tested with increasing correlation order
(correspondent to
variable xi) until it is possible to accept, with a significance level alpha,
that
(corresponding to variable xi_hat) is the minimum
order of correlation necessary to explain the third cumulant
.
is the maximized third cumulant, supposing a Compund
Poisson Process (CPP) model for correlated spike trains (see [1])
with maximum order of correlation equal to
.
Parameters: | data : neo.AnalogSignal
ximax : int
alpha : float
|
---|---|
Returns: | xi_hat : int
p : list
kappa : list
test_aborted : bool
|
References
[1]Staude, Rotter, Gruen, (2009) J. Comp. Neurosci