hxtorch.spiking.Synapse

class hxtorch.spiking.Synapse(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, plasticity_rule: PlasticityRule | None = None, receptor: Literal['excitatory', 'inhibitory'] | List[str] | Tuple[str, ...] | None = None, weight_scale: float = 1.0, mock: bool = False, weight_step: Union[torch.Tensor, float, int, None] = 1.0, weight_bounds: Optional[Bounds] = Bounds(_lower=tensor(-63.), _upper=tensor(63.), device=None, hardware_aware=True, surrogate=functools.partial(<function exponential_rolloff>, rolloff_margin=0.1, rolloff_margin_abs=0.15)), event_drop_transform: Optional[Callable] = None)

Bases: hxtorch.spiking.modules.types.projection.Projection

Synapse layer

Caveat: For execution on hardware, this module can only be used in conjunction with a subsequent Neuron module.

__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, plasticity_rule: PlasticityRule | None = None, receptor: Literal['excitatory', 'inhibitory'] | List[str] | Tuple[str, ...] | None = None, weight_scale: float = 1.0, mock: bool = False, weight_step: Union[torch.Tensor, float, int, None] = 1.0, weight_bounds: Optional[Bounds] = Bounds(_lower=tensor(-63.), _upper=tensor(63.), device=None, hardware_aware=True, surrogate=functools.partial(<function exponential_rolloff>, rolloff_margin=0.1, rolloff_margin_abs=0.15)), event_drop_transform: Optional[Callable] = 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.

  • weight_scale – Scaling factor, with which the weight values are multiplied to transform them from model to hardware domain.

  • mock – Flag that enables the mocking of discrete and finite weights in simulation, similar as on hardware.

  • weight_step – The step size for the discretization, which is to be performed on the weights. The weight values are rounded to the closest multiple of weight_step. If set to None, no weight discretization is performed.

  • weight_bounds – The bounds, to which the values in the weight Tensor are clamped to if they exceed the bounds. Also holds the surrogate function which is used as a replacement for the clamp function. If weight_bounds is set to None, no clamping is performed.

  • event_drop_transform – A function to mock pre-synaptic event drops. Needs to take a torch.Tensor of shape (timesteps, batch size, pre-synaptic population size) that includes the spike events recieved by the synapse layer and return a torch.Tensor of the same shape.

Param

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

Methods

__init__(in_features, out_features, experiment)

TODO: Think about what to do with device here.

forward_func(input)

get_connections()

reset_parameters()

Resets the synapses weights by reinitialization using torch.nn.kaiming_uniform_.

Attributes

changed_input_data

Getter for changed_since_last_run.
property changed_input_data

Getter for changed_since_last_run.

Returns

Boolean indicating wether module changed since last run.

forward_func(input: hxtorch.spiking.handle.Handle_current_membrane_cadc_membrane_madc_spikes) → hxtorch.spiking.handle.Handle_graded_spikes
get_connections() → List[Tuple[int, int, float]]
output_type

alias of hxtorch.spiking.handle.Handle_graded_spikes

reset_parameters() → None

Resets the synapses weights by reinitialization using torch.nn.kaiming_uniform_.