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)[source]¶
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.