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Explainability

Bitstream-level explainability and causal attribution for SC decisions. Anchored to formal verification properties.

Replay Guardrails

ExplainabilityEngine.explain_spike and ExplainabilityEngine.replay_bitstream validate deterministic replay inputs before allocating streams or mutating provenance. threshold_q16 must be an integer in [0, 65535], replay lengths must be positive integers, and spike_threshold_count must be between 0 and the replayed bitstream length. Rejected inputs raise ValueError before any decision node or provenance step is recorded.

Quick Start

Python
from sc_neurocore.explainability.explainability import (
    ExplainabilityEngine, CausalAttributor, FormalPropertyLink,
)

sc_neurocore.explainability.explainability

Bitstream-level explainability with deterministic replay and provenance.

Leverages bit-true deterministic SC streams and LFSR replay to generate full provenance traces: "why this spike fired" as a verifiable bitstream decision tree. Integrates with the predictive_coding.ReasoningTrace for neuro-symbolic explanations and produces hash-verified audit trails.

LFSRReplay

Deterministic LFSR-16 replay engine.

Mirrors core_engine::Lfsr16 polynomial: x^16 + x^14 + x^13 + x^11 + 1. Given the same seed, reproduces the exact same bitstream for formal verification and replay-based auditing.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class LFSRReplay:
    """Deterministic LFSR-16 replay engine.

    Mirrors ``core_engine::Lfsr16`` polynomial: x^16 + x^14 + x^13 + x^11 + 1.
    Given the same seed, reproduces the exact same bitstream for formal
    verification and replay-based auditing.
    """

    def __init__(self, seed: int) -> None:
        if seed == 0:
            raise ValueError("LFSR seed must be non-zero")
        self.initial_seed = seed
        self.reg = seed & 0xFFFF

    def step(self) -> int:
        """Advance one step, return new register value."""
        feedback = ((self.reg >> 15) ^ (self.reg >> 13) ^ (self.reg >> 12) ^ (self.reg >> 10)) & 1
        self.reg = ((self.reg << 1) | feedback) & 0xFFFF
        return self.reg

    def encode(self, threshold: int, length: int) -> np.ndarray[Any, Any]:
        """Generate a bitstream by comparing LFSR output against threshold."""
        bits = np.zeros(length, dtype=np.uint8)
        for i in range(length):
            bits[i] = 1 if self.reg < threshold else 0
            self.step()
        return bits

    def reset(self) -> None:
        """Reset to initial seed for replay."""
        self.reg = self.initial_seed

step()

Advance one step, return new register value.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def step(self) -> int:
    """Advance one step, return new register value."""
    feedback = ((self.reg >> 15) ^ (self.reg >> 13) ^ (self.reg >> 12) ^ (self.reg >> 10)) & 1
    self.reg = ((self.reg << 1) | feedback) & 0xFFFF
    return self.reg

encode(threshold, length)

Generate a bitstream by comparing LFSR output against threshold.

Source code in src/sc_neurocore/explainability/explainability.py
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def encode(self, threshold: int, length: int) -> np.ndarray[Any, Any]:
    """Generate a bitstream by comparing LFSR output against threshold."""
    bits = np.zeros(length, dtype=np.uint8)
    for i in range(length):
        bits[i] = 1 if self.reg < threshold else 0
        self.step()
    return bits

reset()

Reset to initial seed for replay.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def reset(self) -> None:
    """Reset to initial seed for replay."""
    self.reg = self.initial_seed

DecisionMargin dataclass

How close a decision was to flipping.

Source code in src/sc_neurocore/explainability/explainability.py
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@dataclass
class DecisionMargin:
    """How close a decision was to flipping."""

    popcount: int
    threshold: int
    margin: int  # popcount - threshold (negative = no spike)
    confidence: float  # |margin| / bitstream_length

DecisionNode dataclass

One node in a spike decision tree.

Source code in src/sc_neurocore/explainability/explainability.py
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@dataclass
class DecisionNode:
    """One node in a spike decision tree."""

    neuron_id: str
    popcount: int
    threshold: int
    bitstream_length: int
    probability: float
    scc_context: float = 0.0
    scc_influence: float = 0.0
    decision: SpikeDecision = SpikeDecision.UNDETERMINED
    children: List[DecisionNode] = field(default_factory=list)
    bitstream_hash: str = ""
    timestep: int = 0
    threshold_q16: int = 0
    layer_id: str = ""
    contributing_neurons: List[str] = field(default_factory=list)

    @property
    def is_leaf(self) -> bool:
        return len(self.children) == 0

    @property
    def margin(self) -> DecisionMargin:
        m = self.popcount - self.threshold
        conf = abs(m) / self.bitstream_length if self.bitstream_length > 0 else 0.0
        return DecisionMargin(self.popcount, self.threshold, m, conf)

SpikeDecisionTree

Captures "why this spike fired" as a verifiable decision tree.

Source code in src/sc_neurocore/explainability/explainability.py
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class SpikeDecisionTree:
    """Captures "why this spike fired" as a verifiable decision tree."""

    def __init__(self) -> None:
        self.root: Optional[DecisionNode] = None
        self._nodes: List[DecisionNode] = []

    def add_decision(
        self,
        neuron_id: str,
        bitstream: np.ndarray[Any, Any],
        threshold: int,
        scc: float = 0.0,
        parent: Optional[DecisionNode] = None,
        timestep: int = 0,
        layer_id: str = "",
        contributing_neurons: Optional[List[str]] = None,
        threshold_q16: int = 0,
    ) -> DecisionNode:
        """Record a spike decision from bitstream observation."""
        popcount = int(np.sum(bitstream))
        length = len(bitstream)
        prob = popcount / length if length > 0 else 0.0
        decision = SpikeDecision.SPIKE if popcount >= threshold else SpikeDecision.NO_SPIKE
        bs_hash = hashlib.sha256(bitstream.tobytes()).hexdigest()[:16]

        scc_influence = abs(scc) * (popcount / max(length, 1))

        node = DecisionNode(
            neuron_id=neuron_id,
            popcount=popcount,
            threshold=threshold,
            bitstream_length=length,
            probability=prob,
            scc_context=scc,
            scc_influence=scc_influence,
            decision=decision,
            bitstream_hash=bs_hash,
            timestep=timestep,
            layer_id=layer_id,
            contributing_neurons=contributing_neurons or [],
            threshold_q16=threshold_q16,
        )

        if parent is not None:
            parent.children.append(node)
        elif self.root is None:
            self.root = node

        self._nodes.append(node)
        return node

    @property
    def depth(self) -> int:
        if self.root is None:
            return 0
        return self._compute_depth(self.root)

    def _compute_depth(self, node: DecisionNode) -> int:
        if not node.children:
            return 1
        return 1 + max(self._compute_depth(c) for c in node.children)

    @property
    def num_spikes(self) -> int:
        return sum(1 for n in self._nodes if n.decision == SpikeDecision.SPIKE)

    @property
    def num_nodes(self) -> int:
        return len(self._nodes)

    def nodes_at_layer(self, layer_id: str) -> List[DecisionNode]:
        """Return all nodes at a given layer."""
        return [n for n in self._nodes if n.layer_id == layer_id]

    def nodes_at_timestep(self, timestep: int) -> List[DecisionNode]:
        """Return all nodes at a given timestep."""
        return [n for n in self._nodes if n.timestep == timestep]

    def get_node(self, neuron_id: str) -> Optional[DecisionNode]:
        """Look up a node by neuron ID."""
        for n in self._nodes:
            if n.neuron_id == neuron_id:
                return n
        return None

    def spike_path(self) -> List[DecisionNode]:
        """Return the chain of spiking nodes from root down."""
        if self.root is None:
            return []
        path: List[DecisionNode] = []
        self._collect_spike_path(self.root, path)
        return path

    def _collect_spike_path(self, node: DecisionNode, path: List[DecisionNode]) -> None:
        if node.decision == SpikeDecision.SPIKE:
            path.append(node)
        for c in node.children:
            self._collect_spike_path(c, path)

    def to_dict(self) -> Dict[str, Any]:
        if self.root is None:
            return {}
        return self._node_to_dict(self.root)

    def _node_to_dict(self, node: DecisionNode) -> Dict[str, Any]:
        return {
            "neuron_id": node.neuron_id,
            "popcount": node.popcount,
            "threshold": node.threshold,
            "probability": node.probability,
            "decision": node.decision.value,
            "bitstream_hash": node.bitstream_hash,
            "scc_influence": node.scc_influence,
            "margin": node.margin.margin,
            "confidence": node.margin.confidence,
            "timestep": node.timestep,
            "layer_id": node.layer_id,
            "contributing_neurons": node.contributing_neurons,
            "children": [self._node_to_dict(c) for c in node.children],
        }

add_decision(neuron_id, bitstream, threshold, scc=0.0, parent=None, timestep=0, layer_id='', contributing_neurons=None, threshold_q16=0)

Record a spike decision from bitstream observation.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def add_decision(
    self,
    neuron_id: str,
    bitstream: np.ndarray[Any, Any],
    threshold: int,
    scc: float = 0.0,
    parent: Optional[DecisionNode] = None,
    timestep: int = 0,
    layer_id: str = "",
    contributing_neurons: Optional[List[str]] = None,
    threshold_q16: int = 0,
) -> DecisionNode:
    """Record a spike decision from bitstream observation."""
    popcount = int(np.sum(bitstream))
    length = len(bitstream)
    prob = popcount / length if length > 0 else 0.0
    decision = SpikeDecision.SPIKE if popcount >= threshold else SpikeDecision.NO_SPIKE
    bs_hash = hashlib.sha256(bitstream.tobytes()).hexdigest()[:16]

    scc_influence = abs(scc) * (popcount / max(length, 1))

    node = DecisionNode(
        neuron_id=neuron_id,
        popcount=popcount,
        threshold=threshold,
        bitstream_length=length,
        probability=prob,
        scc_context=scc,
        scc_influence=scc_influence,
        decision=decision,
        bitstream_hash=bs_hash,
        timestep=timestep,
        layer_id=layer_id,
        contributing_neurons=contributing_neurons or [],
        threshold_q16=threshold_q16,
    )

    if parent is not None:
        parent.children.append(node)
    elif self.root is None:
        self.root = node

    self._nodes.append(node)
    return node

nodes_at_layer(layer_id)

Return all nodes at a given layer.

Source code in src/sc_neurocore/explainability/explainability.py
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def nodes_at_layer(self, layer_id: str) -> List[DecisionNode]:
    """Return all nodes at a given layer."""
    return [n for n in self._nodes if n.layer_id == layer_id]

nodes_at_timestep(timestep)

Return all nodes at a given timestep.

Source code in src/sc_neurocore/explainability/explainability.py
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def nodes_at_timestep(self, timestep: int) -> List[DecisionNode]:
    """Return all nodes at a given timestep."""
    return [n for n in self._nodes if n.timestep == timestep]

get_node(neuron_id)

Look up a node by neuron ID.

Source code in src/sc_neurocore/explainability/explainability.py
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def get_node(self, neuron_id: str) -> Optional[DecisionNode]:
    """Look up a node by neuron ID."""
    for n in self._nodes:
        if n.neuron_id == neuron_id:
            return n
    return None

spike_path()

Return the chain of spiking nodes from root down.

Source code in src/sc_neurocore/explainability/explainability.py
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def spike_path(self) -> List[DecisionNode]:
    """Return the chain of spiking nodes from root down."""
    if self.root is None:
        return []
    path: List[DecisionNode] = []
    self._collect_spike_path(self.root, path)
    return path

ProvenanceStep dataclass

One step in a full provenance chain.

Source code in src/sc_neurocore/explainability/explainability.py
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@dataclass
class ProvenanceStep:
    """One step in a full provenance chain."""

    stage: str
    description: str
    data_hash: str
    timestamp_ns: int
    metadata: Dict[str, Any] = field(default_factory=dict)

ProvenanceTrace

Full chain from input → encoding → computation → spike decision.

Source code in src/sc_neurocore/explainability/explainability.py
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class ProvenanceTrace:
    """Full chain from input → encoding → computation → spike decision."""

    def __init__(self) -> None:
        self._steps: List[ProvenanceStep] = []
        self._complete = False

    def add_step(
        self,
        stage: str,
        description: str,
        data: Optional[np.ndarray[Any, Any]] = None,
        metadata: Optional[Dict[str, Any]] = None,
    ) -> ProvenanceStep:
        """Record one provenance step."""
        if data is not None:
            data_hash = hashlib.sha256(data.tobytes()).hexdigest()[:16]
        else:
            data_hash = hashlib.sha256(description.encode()).hexdigest()[:16]

        step = ProvenanceStep(
            stage=stage,
            description=description,
            data_hash=data_hash,
            timestamp_ns=time.perf_counter_ns(),
            metadata=metadata or {},
        )
        self._steps.append(step)
        return step

    def finalize(self) -> None:
        self._complete = True

    @property
    def is_complete(self) -> bool:
        return self._complete

    @property
    def num_steps(self) -> int:
        return len(self._steps)

    @property
    def chain_hash(self) -> str:
        """Hash of the entire provenance chain for tamper detection."""
        h = hashlib.sha256()
        for step in self._steps:
            h.update(step.data_hash.encode())
            h.update(step.stage.encode())
        return h.hexdigest()

    def to_list(self) -> List[Dict[str, Any]]:
        return [
            {
                "stage": s.stage,
                "description": s.description,
                "data_hash": s.data_hash,
                "timestamp_ns": s.timestamp_ns,
                "metadata": s.metadata,
            }
            for s in self._steps
        ]

chain_hash property

Hash of the entire provenance chain for tamper detection.

add_step(stage, description, data=None, metadata=None)

Record one provenance step.

Source code in src/sc_neurocore/explainability/explainability.py
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def add_step(
    self,
    stage: str,
    description: str,
    data: Optional[np.ndarray[Any, Any]] = None,
    metadata: Optional[Dict[str, Any]] = None,
) -> ProvenanceStep:
    """Record one provenance step."""
    if data is not None:
        data_hash = hashlib.sha256(data.tobytes()).hexdigest()[:16]
    else:
        data_hash = hashlib.sha256(description.encode()).hexdigest()[:16]

    step = ProvenanceStep(
        stage=stage,
        description=description,
        data_hash=data_hash,
        timestamp_ns=time.perf_counter_ns(),
        metadata=metadata or {},
    )
    self._steps.append(step)
    return step

RegulatoryMetadata dataclass

IEC 62304 / FDA SaMD traceability fields.

Source code in src/sc_neurocore/explainability/explainability.py
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@dataclass
class RegulatoryMetadata:
    """IEC 62304 / FDA SaMD traceability fields."""

    device_class: str = "Class II"
    risk_level: str = "moderate"
    intended_use: str = ""
    software_version: str = ""
    udi: str = ""  # Unique Device Identifier
    sudi_hash: str = ""  # Hash of the software + config
    operator_id: str = ""
    review_status: str = "pending"

Cross-reference to SymbiYosys formal verification.

Source code in src/sc_neurocore/explainability/explainability.py
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@dataclass
class FormalPropertyLink:
    """Cross-reference to SymbiYosys formal verification."""

    property_name: str
    property_file: str = ""
    status: str = "unverified"  # proven / cex / unverified
    bounded_depth: int = 0
    engine: str = "sby"

VerifiabilityReport dataclass

Formal audit report with hash verification.

Source code in src/sc_neurocore/explainability/explainability.py
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@dataclass
class VerifiabilityReport:
    """Formal audit report with hash verification."""

    chain_hash: str
    num_steps: int
    all_hashes_valid: bool
    decision_tree_depth: int
    total_spikes: int
    replay_seed: int
    replay_matches: bool
    regulatory: Optional[RegulatoryMetadata] = None
    formal_properties: List[FormalPropertyLink] = field(default_factory=list)

SensitivityResult dataclass

Result of a 'what-if' threshold perturbation.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@dataclass
class SensitivityResult:
    """Result of a 'what-if' threshold perturbation."""

    neuron_id: str
    original_threshold: int
    perturbed_threshold: int
    original_decision: SpikeDecision
    perturbed_decision: SpikeDecision
    flipped: bool

SensitivityAnalyzer

Counterfactual analysis: 'would the decision flip if threshold ±N?'

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class SensitivityAnalyzer:
    """Counterfactual analysis: 'would the decision flip if threshold ±N?'"""

    @staticmethod
    def analyze(
        node: DecisionNode,
        perturbations: Optional[List[int]] = None,
    ) -> List[SensitivityResult]:
        if perturbations is None:
            perturbations = [-10, -5, -1, 1, 5, 10]
        results = []
        for delta in perturbations:
            new_t = max(0, node.threshold + delta)
            new_dec = SpikeDecision.SPIKE if node.popcount >= new_t else SpikeDecision.NO_SPIKE
            results.append(
                SensitivityResult(
                    neuron_id=node.neuron_id,
                    original_threshold=node.threshold,
                    perturbed_threshold=new_t,
                    original_decision=node.decision,
                    perturbed_decision=new_dec,
                    flipped=(new_dec != node.decision),
                )
            )
        return results

    @staticmethod
    def critical_delta(node: DecisionNode) -> int:
        """Smallest threshold change that flips the decision."""
        m = node.margin
        if m.margin >= 0:
            return m.margin + 1
        return m.margin

critical_delta(node) staticmethod

Smallest threshold change that flips the decision.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@staticmethod
def critical_delta(node: DecisionNode) -> int:
    """Smallest threshold change that flips the decision."""
    m = node.margin
    if m.margin >= 0:
        return m.margin + 1
    return m.margin

CausalAttribution dataclass

Attribution of a spike to upstream neurons.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@dataclass
class CausalAttribution:
    """Attribution of a spike to upstream neurons."""

    target_neuron: str
    attributions: Dict[str, float]  # source_neuron -> contribution weight
    total_contribution: float

    @property
    def top_contributors(self) -> List[Tuple[str, float]]:
        """Sorted (descending) list of contributing neurons."""
        return sorted(self.attributions.items(), key=lambda x: x[1], reverse=True)

top_contributors property

Sorted (descending) list of contributing neurons.

CausalAttributor

Computes causal attribution from input neurons to output spike.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class CausalAttributor:
    """Computes causal attribution from input neurons to output spike."""

    @staticmethod
    def attribute(
        target: DecisionNode,
        input_bitstreams: Dict[str, np.ndarray[Any, Any]],
        weights: Optional[Dict[str, float]] = None,
    ) -> CausalAttribution:
        """Compute per-input-neuron contribution to the target popcount."""
        attribs: Dict[str, float] = {}
        for nid, bs in input_bitstreams.items():
            w = weights.get(nid, 1.0) if weights else 1.0
            contribution = float(np.sum(bs)) * w
            attribs[nid] = contribution
        total = sum(attribs.values())
        return CausalAttribution(
            target_neuron=target.neuron_id,
            attributions=attribs,
            total_contribution=total,
        )

attribute(target, input_bitstreams, weights=None) staticmethod

Compute per-input-neuron contribution to the target popcount.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@staticmethod
def attribute(
    target: DecisionNode,
    input_bitstreams: Dict[str, np.ndarray[Any, Any]],
    weights: Optional[Dict[str, float]] = None,
) -> CausalAttribution:
    """Compute per-input-neuron contribution to the target popcount."""
    attribs: Dict[str, float] = {}
    for nid, bs in input_bitstreams.items():
        w = weights.get(nid, 1.0) if weights else 1.0
        contribution = float(np.sum(bs)) * w
        attribs[nid] = contribution
    total = sum(attribs.values())
    return CausalAttribution(
        target_neuron=target.neuron_id,
        attributions=attribs,
        total_contribution=total,
    )

DiffEntry dataclass

One field that differs between two explanations.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@dataclass
class DiffEntry:
    """One field that differs between two explanations."""

    field: str
    value_a: Any
    value_b: Any

ExplanationDiff

Compares two decision nodes to find divergence points.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class ExplanationDiff:
    """Compares two decision nodes to find divergence points."""

    @staticmethod
    def diff(a: DecisionNode, b: DecisionNode) -> List[DiffEntry]:
        diffs = []
        for attr in [
            "neuron_id",
            "popcount",
            "threshold",
            "bitstream_length",
            "probability",
            "scc_context",
            "decision",
            "bitstream_hash",
        ]:
            va = getattr(a, attr)
            vb = getattr(b, attr)
            if va != vb:
                diffs.append(DiffEntry(attr, va, vb))
        return diffs

TemporalWindow

Records decisions across timesteps for temporal attribution.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class TemporalWindow:
    """Records decisions across timesteps for temporal attribution."""

    def __init__(self) -> None:
        self._windows: Dict[int, List[DecisionNode]] = {}

    def add(self, node: DecisionNode) -> None:
        self._windows.setdefault(node.timestep, []).append(node)

    def spike_rate_at(self, timestep: int) -> float:
        nodes = self._windows.get(timestep, [])
        if not nodes:
            return 0.0
        return sum(1 for n in nodes if n.decision == SpikeDecision.SPIKE) / len(nodes)

    def active_timesteps(self) -> List[int]:
        return sorted(self._windows.keys())

    def peak_timestep(self) -> int:
        """Timestep with the highest spike rate."""
        best_t = 0
        best_rate = -1.0
        for t in self._windows:
            rate = self.spike_rate_at(t)
            if rate > best_rate:
                best_rate = rate
                best_t = t
        return best_t

    @property
    def num_timesteps(self) -> int:
        return len(self._windows)

peak_timestep()

Timestep with the highest spike rate.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def peak_timestep(self) -> int:
    """Timestep with the highest spike rate."""
    best_t = 0
    best_rate = -1.0
    for t in self._windows:
        rate = self.spike_rate_at(t)
        if rate > best_rate:
            best_rate = rate
            best_t = t
    return best_t

NaturalLanguageExplainer

Generates human-readable explanation strings.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class NaturalLanguageExplainer:
    """Generates human-readable explanation strings."""

    @staticmethod
    def explain_node(node: DecisionNode) -> str:
        m = node.margin
        if node.decision == SpikeDecision.SPIKE:
            desc = (
                f"Neuron {node.neuron_id} fired at timestep {node.timestep}. "
                f"Popcount {node.popcount} exceeded threshold {node.threshold} "
                f"by {m.margin} bits (confidence {m.confidence:.1%}). "
                f"Encoded probability was {node.probability:.3f}."
            )
        else:
            desc = (
                f"Neuron {node.neuron_id} did NOT fire at timestep {node.timestep}. "
                f"Popcount {node.popcount} fell short of threshold {node.threshold} "
                f"by {abs(m.margin)} bits. "
                f"Encoded probability was {node.probability:.3f}."
            )
        if node.scc_context > 0:
            desc += f" Correlation context SCC={node.scc_context:.3f} may have biased encoding."
        if node.contributing_neurons:
            desc += f" Driven by inputs: {', '.join(node.contributing_neurons[:5])}."
        return desc

    @staticmethod
    def explain_attribution(attr: CausalAttribution) -> str:
        top = attr.top_contributors[:3]
        parts = [f"{nid} ({w:.1f})" for nid, w in top]
        return (
            f"Spike at {attr.target_neuron} was primarily caused by: "
            f"{', '.join(parts)}. Total input contribution: {attr.total_contribution:.1f}."
        )

    @staticmethod
    def explain_sensitivity(results: List[SensitivityResult]) -> str:
        flips = [r for r in results if r.flipped]
        if not flips:
            return "Decision is robust to all tested perturbations."
        smallest = min(flips, key=lambda r: abs(r.perturbed_threshold - r.original_threshold))
        return (
            f"Decision would flip if threshold changed by "
            f"{smallest.perturbed_threshold - smallest.original_threshold:+d} "
            f"(from {smallest.original_threshold} to {smallest.perturbed_threshold})."
        )

    @staticmethod
    def enhance_with_local_llm(
        text: str,
        *,
        bridge: Any,
        question: str = (
            "Rewrite this deterministic spike explanation for a human operator. "
            "Preserve all numeric facts and keep the wording concise."
        ),
    ) -> str:
        """Enhance a deterministic explanation through the local LLM bridge.

        The caller must supply a configured ``LocalLLMBridge`` instance so this
        module keeps no hard runtime dependency on any local model server.
        """
        response = bridge.chat(f"{question}\n\nBase explanation:\n{text}")
        enhanced: str = response.text
        return enhanced

    @staticmethod
    def explain_node_with_local_llm(
        node: DecisionNode,
        *,
        bridge: Any,
        question: str = (
            "Explain this spike decision for a human operator in two short paragraphs. "
            "Do not change or invent numeric values."
        ),
    ) -> str:
        """Generate a local-LLM-enhanced explanation for one decision node."""
        base = NaturalLanguageExplainer.explain_node(node)
        return NaturalLanguageExplainer.enhance_with_local_llm(
            base,
            bridge=bridge,
            question=question,
        )

enhance_with_local_llm(text, *, bridge, question='Rewrite this deterministic spike explanation for a human operator. Preserve all numeric facts and keep the wording concise.') staticmethod

Enhance a deterministic explanation through the local LLM bridge.

The caller must supply a configured LocalLLMBridge instance so this module keeps no hard runtime dependency on any local model server.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@staticmethod
def enhance_with_local_llm(
    text: str,
    *,
    bridge: Any,
    question: str = (
        "Rewrite this deterministic spike explanation for a human operator. "
        "Preserve all numeric facts and keep the wording concise."
    ),
) -> str:
    """Enhance a deterministic explanation through the local LLM bridge.

    The caller must supply a configured ``LocalLLMBridge`` instance so this
    module keeps no hard runtime dependency on any local model server.
    """
    response = bridge.chat(f"{question}\n\nBase explanation:\n{text}")
    enhanced: str = response.text
    return enhanced

explain_node_with_local_llm(node, *, bridge, question='Explain this spike decision for a human operator in two short paragraphs. Do not change or invent numeric values.') staticmethod

Generate a local-LLM-enhanced explanation for one decision node.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@staticmethod
def explain_node_with_local_llm(
    node: DecisionNode,
    *,
    bridge: Any,
    question: str = (
        "Explain this spike decision for a human operator in two short paragraphs. "
        "Do not change or invent numeric values."
    ),
) -> str:
    """Generate a local-LLM-enhanced explanation for one decision node."""
    base = NaturalLanguageExplainer.explain_node(node)
    return NaturalLanguageExplainer.enhance_with_local_llm(
        base,
        bridge=bridge,
        question=question,
    )

MultiLayerTrace

Traces decisions across network layers.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class MultiLayerTrace:
    """Traces decisions across network layers."""

    def __init__(self) -> None:
        self._layers: Dict[str, List[DecisionNode]] = {}

    def add(self, node: DecisionNode) -> None:
        self._layers.setdefault(node.layer_id, []).append(node)

    @property
    def layer_ids(self) -> List[str]:
        return list(self._layers.keys())

    def spikes_at_layer(self, layer_id: str) -> int:
        return sum(1 for n in self._layers.get(layer_id, []) if n.decision == SpikeDecision.SPIKE)

    def spike_rate_at_layer(self, layer_id: str) -> float:
        nodes = self._layers.get(layer_id, [])
        if not nodes:
            return 0.0
        return sum(1 for n in nodes if n.decision == SpikeDecision.SPIKE) / len(nodes)

    def propagation_path(self) -> List[Dict[str, Any]]:
        """Per-layer spike rate for visualising propagation."""
        return [
            {"layer": lid, "spike_rate": self.spike_rate_at_layer(lid), "count": len(nodes)}
            for lid, nodes in self._layers.items()
        ]

propagation_path()

Per-layer spike rate for visualising propagation.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def propagation_path(self) -> List[Dict[str, Any]]:
    """Per-layer spike rate for visualising propagation."""
    return [
        {"layer": lid, "spike_rate": self.spike_rate_at_layer(lid), "count": len(nodes)}
        for lid, nodes in self._layers.items()
    ]

SymbolicPathStep dataclass

One step in a symbolic decision path.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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@dataclass
class SymbolicPathStep:
    """One step in a symbolic decision path."""

    neuron_id: str
    decision: SpikeDecision
    reason: str

SymbolicPath

Human-readable symbolic path: input → encoding → decision.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class SymbolicPath:
    """Human-readable symbolic path: input → encoding → decision."""

    def __init__(self) -> None:
        self.steps: List[SymbolicPathStep] = []

    def add(self, neuron_id: str, decision: SpikeDecision, reason: str) -> None:
        self.steps.append(SymbolicPathStep(neuron_id, decision, reason))

    @property
    def length(self) -> int:
        return len(self.steps)

    def to_list(self) -> List[Dict[str, str]]:
        return [
            {"neuron": s.neuron_id, "decision": s.decision.value, "reason": s.reason}
            for s in self.steps
        ]

ExplainabilityEngine

End-to-end explainability: replay + decision tree + provenance.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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class ExplainabilityEngine:
    """End-to-end explainability: replay + decision tree + provenance."""

    def __init__(self, seed: int = 0xACE1) -> None:
        self.seed = seed
        self.replay = LFSRReplay(seed)
        self.tree = SpikeDecisionTree()
        self.provenance = ProvenanceTrace()
        self.temporal = TemporalWindow()
        self.multi_layer = MultiLayerTrace()
        self.symbolic = SymbolicPath()
        self._replayed_bitstreams: Dict[str, np.ndarray[Any, Any]] = {}

    def explain_spike(
        self,
        neuron_id: str,
        threshold_q16: int,
        bitstream_length: int,
        spike_threshold_count: int,
        scc: float = 0.0,
        timestep: int = 0,
        layer_id: str = "",
        contributing_neurons: Optional[List[str]] = None,
    ) -> DecisionNode:
        """Explain one spike decision via deterministic replay.

        Replays the LFSR from the current seed state, generates the
        exact bitstream, and records the decision in the tree and trace.
        """
        _validate_replay_parameters(
            threshold_q16=threshold_q16,
            bitstream_length=bitstream_length,
            spike_threshold_count=spike_threshold_count,
        )

        self.provenance.add_step(
            "input",
            f"Neuron {neuron_id}: threshold_q16={threshold_q16}, length={bitstream_length}",
        )

        replay = LFSRReplay(self.seed)
        bitstream = replay.encode(threshold_q16, bitstream_length)
        self._replayed_bitstreams[neuron_id] = bitstream

        self.provenance.add_step(
            "encoding",
            f"LFSR encode seed={self.seed:#06x}, threshold={threshold_q16}",
            data=bitstream,
        )

        node = self.tree.add_decision(
            neuron_id=neuron_id,
            bitstream=bitstream,
            threshold=spike_threshold_count,
            scc=scc,
            timestep=timestep,
            layer_id=layer_id,
            contributing_neurons=contributing_neurons,
            threshold_q16=threshold_q16,
        )

        self.temporal.add(node)
        self.multi_layer.add(node)

        reason = (
            f"popcount({node.popcount}) {'≥' if node.decision == SpikeDecision.SPIKE else '<'} "
            f"threshold({spike_threshold_count})"
        )
        self.symbolic.add(neuron_id, node.decision, reason)

        self.provenance.add_step(
            "decision",
            f"Neuron {neuron_id}: {node.decision.value} (popcount={node.popcount}/{spike_threshold_count})",
            metadata={
                "popcount": node.popcount,
                "threshold": spike_threshold_count,
                "probability": node.probability,
                "margin": node.margin.margin,
                "scc_influence": node.scc_influence,
            },
        )

        return node

    def verify(
        self,
        regulatory: Optional[RegulatoryMetadata] = None,
        formal_properties: Optional[List[FormalPropertyLink]] = None,
    ) -> VerifiabilityReport:
        """Generate a verifiability report with full replay check."""
        self.provenance.finalize()

        all_match = True
        for nid, stored_bs in self._replayed_bitstreams.items():
            fresh = LFSRReplay(self.seed)
            node = self.tree.get_node(nid)
            if node is not None:
                re_bs = fresh.encode(
                    threshold=node.threshold_q16,
                    length=node.bitstream_length,
                )
                if not np.array_equal(
                    stored_bs[: min(len(stored_bs), len(re_bs))],
                    re_bs[: min(len(stored_bs), len(re_bs))],
                ):
                    all_match = False

        return VerifiabilityReport(
            chain_hash=self.provenance.chain_hash,
            num_steps=self.provenance.num_steps,
            all_hashes_valid=all_match,
            decision_tree_depth=self.tree.depth,
            total_spikes=self.tree.num_spikes,
            replay_seed=self.seed,
            replay_matches=all_match,
            regulatory=regulatory,
            formal_properties=formal_properties or [],
        )

    def replay_bitstream(
        self,
        threshold_q16: int,
        length: int,
    ) -> np.ndarray[Any, Any]:
        """Replay a bitstream from the engine's seed (for external comparison)."""
        _validate_replay_parameters(
            threshold_q16=threshold_q16,
            bitstream_length=length,
            length_name="length",
        )
        replay = LFSRReplay(self.seed)
        return replay.encode(threshold_q16, length)

    def sensitivity(
        self,
        node: DecisionNode,
        perturbations: Optional[List[int]] = None,
    ) -> List[SensitivityResult]:
        """Run sensitivity analysis on a decision node."""
        return SensitivityAnalyzer.analyze(node, perturbations)

    def attribute(
        self,
        target: DecisionNode,
        input_bitstreams: Dict[str, np.ndarray[Any, Any]],
        weights: Optional[Dict[str, float]] = None,
    ) -> CausalAttribution:
        """Compute causal attribution for a decision."""
        return CausalAttributor.attribute(target, input_bitstreams, weights)

    def explain_spike_with_local_llm(
        self,
        neuron_id: str,
        threshold_q16: int,
        bitstream_length: int,
        spike_threshold_count: int,
        *,
        bridge: Any,
        scc: float = 0.0,
        timestep: int = 0,
        layer_id: str = "",
        contributing_neurons: Optional[List[str]] = None,
        question: str = (
            "Explain this spike decision for a human operator in two short paragraphs. "
            "Do not change or invent numeric values."
        ),
    ) -> tuple[DecisionNode, str]:
        """Run the deterministic explainability path, then enhance it locally."""
        node = self.explain_spike(
            neuron_id=neuron_id,
            threshold_q16=threshold_q16,
            bitstream_length=bitstream_length,
            spike_threshold_count=spike_threshold_count,
            scc=scc,
            timestep=timestep,
            layer_id=layer_id,
            contributing_neurons=contributing_neurons,
        )
        text = NaturalLanguageExplainer.explain_node_with_local_llm(
            node,
            bridge=bridge,
            question=question,
        )
        return node, text

explain_spike(neuron_id, threshold_q16, bitstream_length, spike_threshold_count, scc=0.0, timestep=0, layer_id='', contributing_neurons=None)

Explain one spike decision via deterministic replay.

Replays the LFSR from the current seed state, generates the exact bitstream, and records the decision in the tree and trace.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def explain_spike(
    self,
    neuron_id: str,
    threshold_q16: int,
    bitstream_length: int,
    spike_threshold_count: int,
    scc: float = 0.0,
    timestep: int = 0,
    layer_id: str = "",
    contributing_neurons: Optional[List[str]] = None,
) -> DecisionNode:
    """Explain one spike decision via deterministic replay.

    Replays the LFSR from the current seed state, generates the
    exact bitstream, and records the decision in the tree and trace.
    """
    _validate_replay_parameters(
        threshold_q16=threshold_q16,
        bitstream_length=bitstream_length,
        spike_threshold_count=spike_threshold_count,
    )

    self.provenance.add_step(
        "input",
        f"Neuron {neuron_id}: threshold_q16={threshold_q16}, length={bitstream_length}",
    )

    replay = LFSRReplay(self.seed)
    bitstream = replay.encode(threshold_q16, bitstream_length)
    self._replayed_bitstreams[neuron_id] = bitstream

    self.provenance.add_step(
        "encoding",
        f"LFSR encode seed={self.seed:#06x}, threshold={threshold_q16}",
        data=bitstream,
    )

    node = self.tree.add_decision(
        neuron_id=neuron_id,
        bitstream=bitstream,
        threshold=spike_threshold_count,
        scc=scc,
        timestep=timestep,
        layer_id=layer_id,
        contributing_neurons=contributing_neurons,
        threshold_q16=threshold_q16,
    )

    self.temporal.add(node)
    self.multi_layer.add(node)

    reason = (
        f"popcount({node.popcount}) {'≥' if node.decision == SpikeDecision.SPIKE else '<'} "
        f"threshold({spike_threshold_count})"
    )
    self.symbolic.add(neuron_id, node.decision, reason)

    self.provenance.add_step(
        "decision",
        f"Neuron {neuron_id}: {node.decision.value} (popcount={node.popcount}/{spike_threshold_count})",
        metadata={
            "popcount": node.popcount,
            "threshold": spike_threshold_count,
            "probability": node.probability,
            "margin": node.margin.margin,
            "scc_influence": node.scc_influence,
        },
    )

    return node

verify(regulatory=None, formal_properties=None)

Generate a verifiability report with full replay check.

Source code in src/sc_neurocore/explainability/explainability.py
Python
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def verify(
    self,
    regulatory: Optional[RegulatoryMetadata] = None,
    formal_properties: Optional[List[FormalPropertyLink]] = None,
) -> VerifiabilityReport:
    """Generate a verifiability report with full replay check."""
    self.provenance.finalize()

    all_match = True
    for nid, stored_bs in self._replayed_bitstreams.items():
        fresh = LFSRReplay(self.seed)
        node = self.tree.get_node(nid)
        if node is not None:
            re_bs = fresh.encode(
                threshold=node.threshold_q16,
                length=node.bitstream_length,
            )
            if not np.array_equal(
                stored_bs[: min(len(stored_bs), len(re_bs))],
                re_bs[: min(len(stored_bs), len(re_bs))],
            ):
                all_match = False

    return VerifiabilityReport(
        chain_hash=self.provenance.chain_hash,
        num_steps=self.provenance.num_steps,
        all_hashes_valid=all_match,
        decision_tree_depth=self.tree.depth,
        total_spikes=self.tree.num_spikes,
        replay_seed=self.seed,
        replay_matches=all_match,
        regulatory=regulatory,
        formal_properties=formal_properties or [],
    )

replay_bitstream(threshold_q16, length)

Replay a bitstream from the engine's seed (for external comparison).

Source code in src/sc_neurocore/explainability/explainability.py
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def replay_bitstream(
    self,
    threshold_q16: int,
    length: int,
) -> np.ndarray[Any, Any]:
    """Replay a bitstream from the engine's seed (for external comparison)."""
    _validate_replay_parameters(
        threshold_q16=threshold_q16,
        bitstream_length=length,
        length_name="length",
    )
    replay = LFSRReplay(self.seed)
    return replay.encode(threshold_q16, length)

sensitivity(node, perturbations=None)

Run sensitivity analysis on a decision node.

Source code in src/sc_neurocore/explainability/explainability.py
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def sensitivity(
    self,
    node: DecisionNode,
    perturbations: Optional[List[int]] = None,
) -> List[SensitivityResult]:
    """Run sensitivity analysis on a decision node."""
    return SensitivityAnalyzer.analyze(node, perturbations)

attribute(target, input_bitstreams, weights=None)

Compute causal attribution for a decision.

Source code in src/sc_neurocore/explainability/explainability.py
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def attribute(
    self,
    target: DecisionNode,
    input_bitstreams: Dict[str, np.ndarray[Any, Any]],
    weights: Optional[Dict[str, float]] = None,
) -> CausalAttribution:
    """Compute causal attribution for a decision."""
    return CausalAttributor.attribute(target, input_bitstreams, weights)

explain_spike_with_local_llm(neuron_id, threshold_q16, bitstream_length, spike_threshold_count, *, bridge, scc=0.0, timestep=0, layer_id='', contributing_neurons=None, question='Explain this spike decision for a human operator in two short paragraphs. Do not change or invent numeric values.')

Run the deterministic explainability path, then enhance it locally.

Source code in src/sc_neurocore/explainability/explainability.py
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def explain_spike_with_local_llm(
    self,
    neuron_id: str,
    threshold_q16: int,
    bitstream_length: int,
    spike_threshold_count: int,
    *,
    bridge: Any,
    scc: float = 0.0,
    timestep: int = 0,
    layer_id: str = "",
    contributing_neurons: Optional[List[str]] = None,
    question: str = (
        "Explain this spike decision for a human operator in two short paragraphs. "
        "Do not change or invent numeric values."
    ),
) -> tuple[DecisionNode, str]:
    """Run the deterministic explainability path, then enhance it locally."""
    node = self.explain_spike(
        neuron_id=neuron_id,
        threshold_q16=threshold_q16,
        bitstream_length=bitstream_length,
        spike_threshold_count=spike_threshold_count,
        scc=scc,
        timestep=timestep,
        layer_id=layer_id,
        contributing_neurons=contributing_neurons,
    )
    text = NaturalLanguageExplainer.explain_node_with_local_llm(
        node,
        bridge=bridge,
        question=question,
    )
    return node, text