pynn_brainscales.brainscales2.standardmodels.cells.SpikeSourcePoisson

class pynn_brainscales.brainscales2.standardmodels.cells.SpikeSourcePoisson(start, rate, duration)

Bases: pynn_brainscales.brainscales2.standardmodels.cells_base.StandardCellType, pyNN.standardmodels.cells.SpikeSourcePoisson

Spike source, generating spikes according to a Poisson process.

__init__(start, rate, duration)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(start, rate, duration)	Initialize self.
add_to_input_generator(population, builder, …)	Add external events to input generator.
add_to_network_graph(population, builder)	Add population to network builder.
can_record(variable[, location])
get_spike_times()	When this function is called for the first time, the spike times for a Poisson stimulation are calculated and saved, so that all neurons connected to it receive the same stimulation.

Attributes

recordable
translations

static add_to_input_generator(population: pyNN.common.populations.Population, builder: _pygrenade_vx_network.InputGenerator, snippet_begin_time, snippet_end_time)

Add external events to input generator. :param population: Population to add featuring this cell’s celltype. :param builder: Input builder to add external events to.

static add_to_network_graph(population: pyNN.common.populations.Population, builder: _pygrenade_vx_network.NetworkBuilder) → _pygrenade_vx_network.PopulationOnNetwork

Add population to network builder. :param population: Population to add featuring this cell’s celltype. :param builder: Network builder to add population to. :return: Descriptor of added population

can_record(variable: str, location=None) → bool

get_spike_times() → List[numpy.ndarray]

When this function is called for the first time, the spike times for a Poisson stimulation are calculated and saved, so that all neurons connected to it receive the same stimulation. When a parameter was changed (compared to the last calculation of the spike time), the times are recalculated.

Returns

(unsorted) spike times for each neuron in the population.

recordable: Final[List[str]] = ['spikes']

translations = {'duration': {'forward_transform': 'duration', 'reverse_transform': 'duration', 'translated_name': 'duration'}, 'rate': {'forward_transform': 'rate', 'reverse_transform': 'rate', 'translated_name': 'rate'}, 'start': {'forward_transform': 'start', 'reverse_transform': 'start', 'translated_name': 'start'}}