Cumulant Based Inference of higher-order Correlation (CuBIC)¶
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.
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Performs the CuBIC analysis (Staude et al., 2010) on a population histogram, calculated from a population of spiking neurons. |
Examples¶
Homogeneous Poisson random spike trains population count histogram third cumulant is explained by the first correlation order (xi=1).
Given a list of spike trains, the analysis comprises the following steps:
compute the population histogram (PSTH) with the desired bin size
>>> import numpy as np
>>> import quantities as pq
>>> from elephant import statistics
>>> from elephant.cubic import cubic
>>> from elephant.spike_train_generation import StationaryPoissonProcess
>>> np.random.seed(10)
>>> spiketrains = [StationaryPoissonProcess(rate=10*pq.Hz,
... t_stop=10 * pq.s).generate_spiketrain() for _ in range(20)]
>>> pop_count = statistics.time_histogram(spiketrains, bin_size=0.1 * pq.s)
apply CuBIC to the population count
>>> xi, p_val, kappa, test_aborted = cubic(pop_count, alpha=0.05)
>>> xi
1
>>> p_val
[0.43014065113883904]
>>> kappa
[20.1, 22.656565656565657, 27.674706246134818]