hxtorch.spiking.modules.synapse.EventPropSynapse

class hxtorch.spiking.modules.synapse.EventPropSynapse(in_features: int, out_features: int, experiment: Experiment, chip_coordinate: Optional[Tuple[grenade.common.ChipOnConnection, grenade.common.ConnectionOnExecutor]] = None, device: str = None, dtype: Type = None, transform: Callable = <function linear_saturating>, plasticity_rule: PlasticityRule | None = None, receptor: Literal['excitatory', 'inhibitory'] | List[str] | Tuple[str, ...] | None = None)

Bases: hxtorch.spiking.modules.synapse.Synapse

__init__(in_features: int, out_features: int, experiment: Experiment, chip_coordinate: Optional[Tuple[grenade.common.ChipOnConnection, grenade.common.ConnectionOnExecutor]] = None, device: str = None, dtype: Type = None, transform: Callable = <function linear_saturating>, plasticity_rule: PlasticityRule | None = None, receptor: Literal['excitatory', 'inhibitory'] | List[str] | Tuple[str, ...] | None = None) → None

TODO: Think about what to do with device here.

Parameters

  • in_features – Size of input dimension.

  • out_features – Size of output dimension.

  • experiment – Experiment to append layer to.

  • chip_coordinate – Chip coordinate this module is placed on.

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

  • dtype – Data type of weight tensor.

  • plasticity_rule – Plasticity rule adjusting this synapse.

Param

receptor: Receptor type of the synapse. Can be ‘excitatory’, ‘inhibitory’ or (‘excitatory’, inhibitory’) for a signed synapse.

Methods

forward_func(input)

Attributes

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