hxtorch.spiking.modules.synapse.EventPropSynapse

class hxtorch.spiking.modules.synapse.EventPropSynapse(in_features: int, out_features: int, experiment: Experiment, execution_instance: Optional[ExecutionInstance] = None, chip_coordinate: Optional[DLSGlobal] = None, device: str = None, dtype: Type = None, transform: Callable = <function linear_saturating>)

Bases: hxtorch.spiking.modules.synapse.Synapse

__init__(in_features: int, out_features: int, experiment: Experiment, execution_instance: Optional[ExecutionInstance] = None, chip_coordinate: Optional[DLSGlobal] = None, device: str = None, dtype: Type = None, transform: Callable = <function linear_saturating>) → None

TODO: Think about what to do with device here.

Parameters

• in_features – Size of input dimension.

• out_features – Size of output dimension.

• device – Device to execute on. Only considered in mock-mode.

• dtype – Data type of weight tensor.

• experiment – Experiment to append layer to.

• execution_instance – Execution instance to place to.

• chip_coordinate – Chip coordinate this module is placed on.

Methods

forward_func(input)

Attributes

forward_func(input: types.Handle_current_membrane_cadc_membrane_madc_spikes) → types.Handle_graded_spikes