elephant.spike_train_generation.StationaryPoissonProcess¶
- class elephant.spike_train_generation.StationaryPoissonProcess(rate: Quantity, t_start: Quantity = array(0.) * ms, t_stop: Quantity = array(1000.) * ms, refractory_period: Quantity | None = None, equilibrium: bool = True)[source]¶
Generates spike trains whose spikes are realizations of a stationary Poisson process with the given rate, starting at time t_start and stopping at time t_stop (Deger et al., 2012). Optionally, an absolute refractory period / dead time can be specified.
- Parameters:
- ratepq.Quantity
The constant firing rate.
- 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
- refractory_periodpq.Quantity, optional
The time period after one spike in which no other spike is emitted. This can be called an absolute refractory period or a dead time as used in (Deger et al., 2012). Default : None
- 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.
If refractory_period is not of type pq.Quantity nor None.
If the period between two successive spikes (1 / rate) is smaller or equal than the refractory_period.
Examples
>>> import quantities as pq # noqa >>> from elephant.spike_train_generation import StationaryPoissonProcess >>> spiketrain = StationaryPoissonProcess(rate=50.*pq.Hz,t_stop=1000*pq.ms,t_start=0*pq.ms).generate_spiketrain() >>> spiketrain_array = StationaryPoissonProcess(rate=20*pq.Hz,t_stop=10000*pq.ms,t_start=5000*pq.ms).generate_spiketrain(as_array=True) >>> spiketrain = StationaryPoissonProcess(rate=50*pq.Hz,t_stop=1000*pq.ms,t_start=0*pq.ms,refractory_period=3*pq.ms).generate_spiketrain()
Methods
__init__
(rate[, 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.
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.