elephant.unitary_event_analysis.jointJ_window_analysis¶

elephant.unitary_event_analysis.
jointJ_window_analysis
(spiketrains, bin_size=array(5.) * ms, win_size=array(100.) * ms, win_step=array(5.) * ms, pattern_hash=None, method='analytic_TrialByTrial', t_start=None, t_stop=None, binary=True, n_surrogates=100)[source]¶ Calculates the joint surprise in a sliding window fashion.
Implementation is based on [ue1].
Parameters:  spiketrainslist
 A list of spike trains (neo.SpikeTrain objects) in different trials:
 0axis –> Trials
 1axis –> Neurons
 2axis –> Spike times
 bin_sizepq.Quantity, optional
The size of bins for discretizing spike trains. Default: 5 ms
 win_sizepq.Quantity, optional
The size of the window of analysis. Default: 100 ms
 win_steppq.Quantity, optional
The size of the window step. Default: 5 ms
 pattern_hashint or list of int or None, optional
A list of interested patterns in hash values (see hash_from_pattern and inverse_hash_from_pattern functions). If None, all neurons are participated. Default: None
 methodstr, optional
 The method with which to compute the unitary events:
 ‘analytic_TrialByTrial’: calculate the analytical expectancy on each trial, then sum over all trials;
 ‘analytic_TrialAverage’: calculate the expectancy by averaging over trials (cf. Gruen et al. 2003);
 ‘surrogate_TrialByTrial’: calculate the distribution of expected coincidences by spike time randomization in each trial and sum over trials.
Default: ‘analytic_trialByTrial’
 t_start, t_stopfloat or pq.Quantity, optional
The start and stop times to use for the time points. If not specified, retrieved from the t_start and t_stop attributes of the input spiketrains.
 binarybool, optional
Binarize the binned spike train objects (True) or not. Only the binary matrices are supported at the moment. Default: True
 n_surrogatesint, optional
The number of surrogates to be used. Default: 100
Returns:  dict
The values of the following keys have the shape of
 different window –> 0axis
 different pattern hash –> 1axis
 ‘Js’: list of float
JointSurprise of different given patterns within each window.
 ‘indices’: list of list of int
A list of indices of pattern within each window.
 ‘n_emp’: list of int
The empirical number of each observed pattern.
 ‘n_exp’: list of float
The expected number of each pattern.
 ‘rate_avg’: list of float
The average firing rate of each neuron.
Additionally, ‘input_parameters’ key stores the input parameters.
Raises:  ValueError
If data is not in the format, specified above.
 NotImplementedError
If binary is not True. The method works only with binary matrices at the moment.
Warns:  UserWarning
The ratio between winsize or winstep and bin_size is not an integer.