elephant.spike_train_generation.homogeneous_poisson_process

elephant.spike_train_generation.homogeneous_poisson_process(rate, t_start=array(0.) * ms, t_stop=array(1000.) * ms, as_array=False, refractory_period=None)

Returns a spike train whose spikes are a realization of a Poisson process with the given rate, starting at time t_start and stopping time t_stop.

All numerical values should be given as Quantities, e.g. 100*pq.Hz.

Parameters:

ratepq.Quantity

The rate of the discharge.

t_startpq.Quantity, optional

The beginning of the spike train. Default: 0 * pq.ms

t_stoppq.Quantity, optional

The end of the spike train. Default: 1000 * pq.ms

as_arraybool, optional

If True, a NumPy array of sorted spikes is returned, rather than a neo.SpikeTrain object. Default: False

refractory_periodpq.Quantity or None, optional

pq.Quantity scalar with dimension time. The time period after one spike no other spike is emitted. Default: None

Returns:

spiketrainneo.SpikeTrain or np.ndarray

Homogeneous Poisson process realization, stored in neo.SpikeTrain if as_array is False (default) and np.ndarray otherwise.

Raises:

ValueError

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

If refractory_period is not None or not of type pq.Quantity.

If refractory_period is not None and the period between two successive spikes (1 / rate) is smaller than the refractory_period.

Examples

>>> import quantities as pq
>>> spikes = homogeneous_poisson_process(50*pq.Hz, t_start=0*pq.ms,
...     t_stop=1000*pq.ms)
>>> spikes = homogeneous_poisson_process(
...     20*pq.Hz, t_start=5000*pq.ms, t_stop=10000*pq.ms, as_array=True)
>>> spikes = homogeneous_poisson_process(50*pq.Hz, t_start=0*pq.ms,
...     t_stop=1000*pq.ms, refractory_period = 3*pq.ms)