Branch-ELM compatibility

Module contract

BranchELM and BranchELMConfig provide the baseline and checkpoint-compatible branched neuron implementation. Inputs use (batch, time, input_channels); the standard two-channel readout contains a spike logit and a soma target coordinate.

Source revision: 306a51ed950b. Public export index.

BranchELMConfig

Source

BranchELMConfig(input_dim: int = 1278, memory_units: int = 30, num_branches: int = 32, hidden_units: int = 64, synapse_decay: float = 0.85, memory_decay: float = 0.9) -> None

Fields

Parameter	Type	Default
input_dim	int	1278
memory_units	int	30
num_branches	int	32
hidden_units	int	64
synapse_decay	float	0.85
memory_decay	float	0.9

input_dim: Native input-channel count.

BranchELMConfig.branch_elm_30

Source

@classmethod
branch_elm_30(cls, *, input_dim: int=1278, num_branches: int=32) -> 'BranchELMConfig'

Parameter	Type	Default
input_dim	int	1278
num_branches	int	32

input_dim: Native input-channel count.

Returns 'BranchELMConfig'.

BranchELM

Source

Branch-factorized recurrent surrogate with spike and soma outputs.

Bases: nn.Module.

BranchELM.init

Source

__init__(self, config: BranchELMConfig) -> None

Parameter	Type	Default
config	BranchELMConfig	required

Returns None.

BranchELM.forward

Source

forward(self, x: torch.Tensor) -> torch.Tensor

x must be (B,T,config.input_dim). Returns (B,T,2), with one spike logit and one soma target coordinate per step. Synaptic and memory states start at zero for each call.

Parameter	Type	Default
x	torch.Tensor	required

Returns torch.Tensor.

BranchELM.parameter_count

Source

parameter_count(self) -> int

Returns int.