pynn_brainscales.brainscales2.standardmodels.cells.SpikeSourcePoissonOnChip
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class
pynn_brainscales.brainscales2.standardmodels.cells.SpikeSourcePoissonOnChip(rate, seed) Bases:
pynn_brainscales.brainscales2.standardmodels.cells_base.StandardCellTypeSpike source, generating spikes according to a Poisson process.
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__init__(rate, seed) Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(rate, seed)Initialize self.
can_record(variable[, location])generate_input_data(population, experiment, …)Generate input data for this population.
generate_vertex(population)Generate vertex representation for this population.
Attributes
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background_source_clock_freq: ClassVar[float] = 250000000.0
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can_record(variable: str, location=None) → bool
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generate_input_data(population: pyNN.common.populations.Population, experiment: pygrenade_vx.network.abstract.frontend.ExperimentSnippet, snippet_begin_time: float, snippet_end_time: float) → Dict[int, pygrenade_common.PortData] Generate input data for this population. :param population: Population featuring this cell’s celltype :param experiment: Experiment snippet to generate data for :param snippet_begin_time: Begin time of snippet :param snippet_end_time: End time of snippet :return: Population input data
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static
generate_vertex(population: pyNN.common.populations.Population) → pygrenade_common.Population Generate vertex representation for this population. :param population: Population featuring this cell’s celltype :return: Population vertex
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recordable: Final[List[str]] = ['spikes']
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translations= {'rate': {'forward_transform': 'rate', 'reverse_transform': 'rate', 'translated_name': 'rate', 'type': 'simple'}, 'seed': {'forward_transform': 'seed', 'reverse_transform': 'seed', 'translated_name': 'seed', 'type': 'simple'}}
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