elephant.unitary_event_analysis.jointJ_window_analysis

elephant.unitary_event_analysis.jointJ_window_analysis(data, binsize, winsize, winstep, pattern_hash, method='analytic_TrialByTrial', t_start=None, t_stop=None, binary=True, n_surr=100)

Calculates the joint surprise in a sliding window fashion.

Implementation is based on [ue1].

Parameters:

datalist

A list of spike trains (neo.SpikeTrain objects) in different trials:

0-axis –> Trials

1-axis –> Neurons

2-axis –> Spike times

binsizepq.Quantity

The size of bins for discretizing spike trains.

winsizepq.Quantity

The size of the window of analysis.

winsteppq.Quantity

The size of the window step.

pattern_hashlist of int

list of interested patterns in hash values (see hash_from_pattern and inverse_hash_from_pattern functions)

methodstr

The method with which the unitary events whould be computed

‘analytic_TrialByTrial’ – > calculate the expectency (analytically) on each trial, then sum over all trials.

‘analytic_TrialAverage’ – > calculate the expectency by averaging over trials (cf. Gruen et al. 2003).

‘surrogate_TrialByTrial’ – > calculate the distribution of expected coincidences by spike time randomzation in each trial and sum over trials.

Default is ‘analytic_trialByTrial’

t_startfloat or pq.Quantity, optional

The start time to use for the time points. If not specified, retrieved from the t_start attribute of spiketrains.

t_stopfloat or pq.Quantity, optional

The start time to use for the time points. If not specified, retrieved from the t_stop attribute of spiketrains.

n_surrint, optional

The number of surrogates to be used. Default is 100.

Returns:

dict

The values of each key has the shape of

different pattern hash –> 0-axis

different window –> 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.

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 binsize is not an integer.