elephant.cubic.cubic

elephant.cubic.cubic(histogram, max_iterations=100, alpha=0.05)

Performs the CuBIC analysis (Staude et al., 2010) on a population histogram, calculated from a population of spiking neurons.

The null hypothesis \(H_0: k_3(data)<=k^*_{3,\xi}\) is iteratively tested with increasing correlation order \(\xi\) until it is possible to accept, with a significance level alpha, that \(\hat{\xi}\) is the minimum order of correlation necessary to explain the third cumulant \(k_3(data)\).

\(k^*_{3,\xi}\) is the maximized third cumulant, supposing a Compound Poisson Process (CPP) model for correlated spike trains (see the paper) with maximum order of correlation equal to \(\xi\).

Parameters:

histogramneo.AnalogSignal

The population histogram (count of spikes per time bin) of the entire population of neurons.

max_iterationsint, optional

The maximum number of iterations of the hypothesis test. Corresponds to the \(\hat{\xi_{\text{max}}}\) in (Staude et al., 2010). If it is not possible to compute the \(\hat{\xi}\) before max_iterations iteration, the CuBIC procedure is aborted. Default: 100

alphafloat, optional

The significance level of the hypothesis tests performed. Default: 0.05

Returns:

xi_hatint

The minimum correlation order estimated by CuBIC, necessary to explain the value of the third cumulant calculated from the population.

plist

The ordered list of all the p-values of the hypothesis tests that have been performed. If the maximum number of iteration max_iterations is reached, the last p-value is set to -4.

kappalist

The list of the first three cumulants of the data.

test_abortedbool

Whether the test was aborted because reached the maximum number of iteration, max_iterations.