hxtorch.spiking.modules.types.Population

class hxtorch.spiking.modules.types.Population(size: int, experiment: Experiment, execution_instance: Optional[ExecutionInstance] = None, **hxparams: Dict[str, ModuleParameterType])

Bases: hxtorch.spiking.modules.types.BasePopulation

Base class for on-chip populations on BSS-2

__init__(size: int, experiment: Experiment, execution_instance: Optional[ExecutionInstance] = None, **hxparams: Dict[str, ModuleParameterType]) → None

Parameters

size – Number of input neurons.
experiment – Experiment to append layer to.
execution_instance – Execution instance to place to.

Methods

`__init__`(size, experiment[, execution_instance])	param size Number of input neurons.
`calib_changed_since_last_run`()
`calibration_from_params`(targets, neurons)	Add population specific calibration targets to the experiment-wide calibration target, which holds information for all populations.
`extra_repr`()	Add additional information
`params_dict`()
`params_from_calibration`(targets, neurons)

Attributes

calib_changed_since_last_run() → bool

calibration_from_params(targets: SpikingCalibTarget, neurons) → SpikingCalibTarget

Add population specific calibration targets to the experiment-wide calibration target, which holds information for all populations.

Parameters: spiking_calib_target – Calibration target parameters of all neuron populations registered in the self.experiment instance.
Returns: The chip_wide_calib_target with adjusted parameters.

extra_repr() → str: Add additional information

params_dict() → Dict

params_from_calibration(targets: SpikingCalibTarget, neurons) → None

size: int

training: bool