LIF layer class

Spiking 1D LIF layer class

class src.model.SpikingLayer.LIF1d(output_dim: int, truncated_bptt_ratio: int, membrane_threshold, alpha, beta, train_neuron_parameters_flag: bool, spike_fn: Callable, reset_mode: str, detach_reset: bool, recurrent_flag: bool, decay_input: bool, device, dtype)

Bases: NeuronModel

Class that implements 1D-LIF neurons layer.

multi_step_neuron(input_: Tensor) → Tuple[Tensor, Tensor | List]

training: bool

Non Spiking 1D LIF layer class

class src.model.SpikingLayer.LI1d(output_dim: int, truncated_bptt_ratio: int, alpha, beta, train_neuron_parameters_flag: bool, decay_input: bool, device, dtype)

Bases: MPNeuronModel

Class that implements 1D-LI neurons layer.

multi_step_LI(input_: Tensor) → Tensor

training: bool

Spiking 2D LIF layer class

class src.model.SpikingLayer.LIF2d(output_dim: int, output_channels: int, truncated_bptt_ratio: int, membrane_threshold, alpha, beta, train_neuron_parameters_flag: bool, spike_fn: Callable, reset_mode: str, detach_reset: bool, recurrent_flag: bool, decay_input: bool, device, dtype)

Bases: NeuronModel

Class that implements 2D-LIF neurons layer.

multi_step_neuron(input_: Tensor) → Tuple[Tensor, Tensor | List]

training: bool

Non Spiking 2D LIF layer class

class src.model.SpikingLayer.LI2d(output_dim: int, output_channels: int, truncated_bptt_ratio: int, alpha, beta, train_neuron_parameters_flag: bool, decay_input: bool, device, dtype)

Bases: MPNeuronModel

Class that implements 2D-LI neurons layer.

multi_step_LI(input_: Tensor) → Tensor

training: bool