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 (Grün et al., 1999).
- Parameters:
- spiketrainslist
- A list of spike trains (neo.SpikeTrain objects) in different trials:
0-axis –> Trials
1-axis –> Neurons
2-axis –> 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 –> 0-axis
different pattern hash –> 1-axis
- ‘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.