Activity and experimental control

Module contract

Threshold functions convert model-generated spike logits into events using fixed declared thresholds. Patch-phase layout helpers organize native cadence. HomeostaticThresholdController is an experimental optional code feature; it is not required by the lifecycle guides and is not a contribution presented in the technical report.

Source revision: 306a51ed950b. Public export index.

StratifiedPatchPhaseLayout

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Deterministic patch phases and a phase-major storage permutation.

StratifiedPatchPhaseLayout(phase_ids: torch.Tensor, storage_permutation: torch.Tensor, inverse_permutation: torch.Tensor, phase_population_sizes: tuple[int, ...], storage_group_offsets: tuple[int, ...], storage_group_shape: tuple[int, int]) -> None

Fields

Parameter	Type	Default
phase_ids	torch.Tensor	required
storage_permutation	torch.Tensor	required
inverse_permutation	torch.Tensor	required
phase_population_sizes	tuple[int, ...]	required
storage_group_offsets	tuple[int, ...]	required
storage_group_shape	tuple[int, int]	required

StratifiedPatchPhaseLayout.patch_size

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StratifiedPatchPhaseLayout.patch_size: int

Read-only property. Access as instance.patch_size; do not call it as a function.

Returns int.

StratifiedPatchPhaseLayout.population_size

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StratifiedPatchPhaseLayout.population_size: int

Read-only property. Access as instance.population_size; do not call it as a function.

Returns int.

StratifiedPatchPhaseLayout.phase_sizes

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StratifiedPatchPhaseLayout.phase_sizes: tuple[int, ...]

Read-only property. Access as instance.phase_sizes; do not call it as a function.

Returns tuple[int, ...].

FixedActivityThresholds

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Fixed spike-score thresholds stratified by role and morphology.

FixedActivityThresholds(excitatory: tuple[float, ...], inhibitory: tuple[float, ...]) -> None

Fields

Parameter	Type	Default
excitatory	tuple[float, ...]	required
inhibitory	tuple[float, ...]	required

FixedActivityThresholds.morphology_count

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FixedActivityThresholds.morphology_count: int

Read-only property. Access as instance.morphology_count; do not call it as a function.

Returns int.

FixedActivityThresholds.as_tensor

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as_tensor(self, *, device: torch.device | str, dtype: torch.dtype) -> torch.Tensor

Parameter	Type	Default
device	torch.device | str	required
dtype	torch.dtype	required

device: Execution or allocation device. dtype: Floating-point execution or allocation dtype.

Returns torch.Tensor.

FixedBlockActivityThresholds

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Fixed role/morphology thresholds for each forecast position.

FixedBlockActivityThresholds(forecast_steps: tuple[FixedActivityThresholds, ...]) -> None

Fields

Parameter	Type	Default
forecast_steps	tuple[FixedActivityThresholds, ...]	required

FixedBlockActivityThresholds.forecast_step_count

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FixedBlockActivityThresholds.forecast_step_count: int

Read-only property. Access as instance.forecast_step_count; do not call it as a function.

Returns int.

FixedBlockActivityThresholds.morphology_count

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FixedBlockActivityThresholds.morphology_count: int

Read-only property. Access as instance.morphology_count; do not call it as a function.

Returns int.

FixedBlockActivityThresholds.as_tensor

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as_tensor(self, *, device: torch.device | str, dtype: torch.dtype) -> torch.Tensor

Parameter	Type	Default
device	torch.device | str	required
dtype	torch.dtype	required

device: Execution or allocation device. dtype: Floating-point execution or allocation dtype.

Returns torch.Tensor.

HomeostaticThresholdController

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Adapt group thresholds to oppose sustained firing-rate errors.

HomeostaticThresholdController.init

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__init__(self, *, group_ids: torch.Tensor, target_rates_hz: torch.Tensor, dt_ms: float, update_interval_ms: float, time_constant_ms: float, learning_rate: float, max_abs_offset: float) -> None

Experimental optional feature. group_ids assigns neurons to declared groups; target_rates_hz supplies each group’s target. A causal group-rate estimate drives bounded threshold offsets. This interface does not establish biological timescales or biological realism.

Parameter	Type	Default
group_ids	torch.Tensor	required
target_rates_hz	torch.Tensor	required
dt_ms	float	required
update_interval_ms	float	required
time_constant_ms	float	required
learning_rate	float	required
max_abs_offset	float	required

learning_rate: Optimizer step size.

Returns None.

HomeostaticThresholdController.observe

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@torch.no_grad()
observe(self, activity: torch.Tensor) -> bool

Observe one simulation step and update at the window boundary.

Observes one neuron activity vector for a simulation step; returns whether the update interval triggered an offset update. No future activity is used.

Parameter	Type	Default
activity	torch.Tensor	required

Returns bool.

HomeostaticThresholdController.threshold_offsets_per_neuron

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threshold_offsets_per_neuron(self) -> torch.Tensor

Return the current group offset for every population member.

Returns one current group-derived threshold offset per neuron.

Returns torch.Tensor.

build_stratified_patch_phase_layout

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build_stratified_patch_phase_layout(morphology_ids: torch.Tensor, inhibitory: torch.Tensor, *, patch_size: int, seed: int, tile_ids: torch.Tensor | None=None) -> StratifiedPatchPhaseLayout

Balance phases independently inside every morphology and E/I group.

Parameter	Type	Default
morphology_ids	torch.Tensor	required
inhibitory	torch.Tensor	required
patch_size	int	required
seed	int	required
tile_ids	torch.Tensor | None	None

morphology_ids: Ordered morphology identity vocabulary. patch_size: Native timesteps represented by one block. seed: Random seed for the declared operation.

Returns StratifiedPatchPhaseLayout.

threshold_model_activity

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threshold_model_activity(spike_scores: torch.Tensor, morphology_ids: torch.Tensor, inhibitory: torch.Tensor, thresholds: FixedActivityThresholds) -> torch.Tensor

Return variable-cardinality events from fixed model-score crossings.

Parameter	Type	Default
spike_scores	torch.Tensor	required
morphology_ids	torch.Tensor	required
inhibitory	torch.Tensor	required
thresholds	FixedActivityThresholds	required

morphology_ids: Ordered morphology identity vocabulary.

Returns torch.Tensor.

threshold_block_model_activity

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threshold_block_model_activity(spike_scores: torch.Tensor, morphology_ids: torch.Tensor, inhibitory: torch.Tensor, thresholds: FixedBlockActivityThresholds) -> torch.Tensor

Threshold an ordered forecast block without fixing event counts.

Parameter	Type	Default
spike_scores	torch.Tensor	required
morphology_ids	torch.Tensor	required
inhibitory	torch.Tensor	required
thresholds	FixedBlockActivityThresholds	required

morphology_ids: Ordered morphology identity vocabulary.

Returns torch.Tensor.