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)

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.