elephant.spade.pvalue_spectrum¶

elephant.spade.
pvalue_spectrum
(spiketrains, bin_size, winlen, dither, n_surr, min_spikes=2, min_occ=2, max_spikes=None, max_occ=None, min_neu=1, spectrum='#', surr_method='dither_spikes', **surr_kwargs)[source]¶ Compute the pvalue spectrum of pattern signatures extracted from surrogates of parallel spike trains, under the null hypothesis of independent spiking.
 n_surr surrogates are obtained from each spike train by spike dithering
 pattern candidates (concepts) are collected from each surrogate data
 the signatures (number of spikes, number of occurrences) of all patterns are computed, and their occurrence probability estimated by their occurrence frequency (pvalue spectrum)
Parameters:  spiketrainslist of neo.SpikeTrain
List containing the parallel spike trains to analyze
 bin_sizepq.Quantity
The time precision used to discretize the spiketrains (binning).
 winlenint
The size (number of bins) of the sliding window used for the analysis. The maximal length of a pattern (delay between first and last spike) is then given by winlen*bin_size
 ditherpq.Quantity
Amount of spike time dithering for creating the surrogates for filtering the pattern spectrum. A spike at time t is placed randomly within [tdither, t+dither] (see also
elephant.spike_train_surrogates.dither_spikes()
). Default: 15*pq.s n_surrint
Number of surrogates to generate to compute the pvalue spectrum. This number should be large (n_surr>=1000 is recommended for 100 spike trains in spiketrains). If n_surr is 0, then the pvalue spectrum is not computed. Default: 0
 min_spikesint, optional
Minimum number of spikes of a sequence to be considered a pattern. Default: 2
 min_occint, optional
Minimum number of occurrences of a sequence to be considered as a pattern. Default: 2
 max_spikesint, optional
Maximum number of spikes of a sequence to be considered a pattern. If None no maximal number of spikes is considered. Default: None
 max_occint, optional
Maximum number of occurrences of a sequence to be considered as a pattern. If None, no maximal number of occurrences is considered. Default: None
 min_neuint, optional
Minimum number of neurons in a sequence to considered a pattern. Default: 1
 spectrum{‘#’, ‘3d#’}, optional
Defines the signature of the patterns.
 ‘#’: pattern spectrum using the as signature the pair:
(number of spikes, number of occurrence)
 ‘3d#’: pattern spectrum using the as signature the triplets:
(number of spikes, number of occurrence, difference between last and first spike of the pattern)
Default: ‘#’
 surr_methodstr
Method that is used to generate the surrogates. You can use every method defined in
elephant.spike_train_surrogates.dither_spikes()
. Default: ‘dither_spikes’ surr_kwargs
Keyword arguments that are passed to the surrogate methods.
Returns:  pv_speclist
 if spectrum == ‘#’:
A list of triplets (z,c,p), where (z,c) is a pattern signature and p is the corresponding pvalue (fraction of surrogates containing signatures (z*,c*)>=(z,c)).
 if spectrum == ‘3d#’:
A list of triplets (z,c,l,p), where (z,c,l) is a pattern signature and p is the corresponding pvalue (fraction of surrogates containing signatures (z*,c*,l*)>=(z,c,l)).
Signatures whose empirical pvalue is 0 are not listed.