elephant.spike_train_generation.StationaryLogNormalProcess

class elephant.spike_train_generation.StationaryLogNormalProcess(rate: Quantity, sigma: float, t_start: Quantity = array(0.) * s, t_stop: Quantity = array(1.) * s, equilibrium: bool = True)

Generates spike trains whose spikes are realizations of a stationary LogNormal process with the given rate and sigma starting at time t_start and stopping at time t_stop.

Parameters:

ratepq.Quantity

The constant firing rate.

sigmafloat

The sigma/ s parameter of the Log-Normal distribution.

t_startpq.Quantity, optional

The start of the spike train. Default: 0.*pq.s

t_stoppq.Quantity, optional

The end of the spike train. Default: 1.*pq.s

equilibriumbool, optional

Generate an equilibrium or an ordinary renewal process. Default: True

Raises:

ValueError

If one of rate, t_start and t_stop is not of type pq.Quantity.

Examples

>>> import quantities as pq
>>> spiketrain = StationaryLogNormalProcess(
...     rate=50*pq.Hz, sigma=2.0, t_start=0*pq.ms,
...     t_stop=1000*pq.ms).generate_spiketrain()
>>> spiketrain_array = StationaryLogNormalProcess(
...     rate=20*pq.Hz, sigma=5.0, t_start=5000*pq.ms,
...     t_stop=10000*pq.ms).generate_spiketrain(as_array=True)

Methods

__init__(rate, sigma[, t_start, t_stop, ...])
generate_n_spiketrains(n_spiketrains[, as_array])	Generates a list of spike trains.
generate_spiketrain([as_array])	Generates a single spike train.

Attributes

expected_cv	The expected coefficient of variation given the ISI distribution.
mu	The parameter mu of the log-normal distribution.
t_start	t_start quantity; there are no spike times below this value.
t_stop	t_stop quantity; there are no spike times above this value.