hxtorch.spiking.modules.types.Population

class hxtorch.spiking.modules.types.Population(size: int, experiment: Experiment, func: Union[Callable, torch.autograd.Function], execution_instance: Optional[ExecutionInstance] = None)

Bases: hxtorch.spiking.modules.hx_module.HXModule

Base class for populations on BSS-2

__init__(size: int, experiment: Experiment, func: Union[Callable, torch.autograd.Function], execution_instance: Optional[ExecutionInstance] = None) → None
Parameters

  • size – Number of input neurons.

  • experiment – Experiment to append layer to.

  • execution_instance – Execution instance to place to.

  • func – Callable function implementing the module’s forward functionality or a torch.autograd.Function implementing the module’s forward and backward operation. TODO: Inform about func args

Methods

__init__(size, experiment, func[, …])

param size

Number of input neurons.

calib_changed_since_last_run()

calibration_from_params(spiking_calib_target)

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

extra_repr()

Add additional information

params_from_calibration(spiking_calib_target)

Attributes

calib_changed_since_last_run() → bool
calibration_from_params(spiking_calib_target: SpikingCalibTarget) → Dict

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_args: Tuple[Any]
extra_kwargs: Dict[str, Any]
extra_repr() → str

Add additional information

params_from_calibration(spiking_calib_target: SpikingCalibTarget) → None
size: int
training: bool