Module pattern_detection 

Pattern-Triggered Replay Detection (PIPE-2)

Implements biologically-inspired pattern detection for memory consolidation. Instead of fixed-interval replay, this module detects meaningful patterns that should trigger immediate consolidation.

Neuroscience Basis

Based on hippocampal sharp-wave ripple research (Rasch & Born 2013):

• Consolidation is triggered by coherent neural activity patterns
• High-value memories and semantically related clusters get priority
• Emotional significance (salience spikes) triggers immediate replay

Pattern Types

1. Entity Co-occurrence: Memories sharing multiple named entities
2. Semantic Clustering: Dense groups of semantically similar memories
3. Temporal Clustering: Memories from the same session/timeframe
4. Salience Spikes: High importance/arousal memories
5. Behavioral Changes: Topic switches, user corrections

Structs

BehavioralContext

Behavioral context tracking

EntityPatternStats

Statistics for an entity group pattern

PatternDetectionResult

Result of pattern detection cycle

PatternDetector

Pattern detector for triggering memory replay

PatternDetectorStats

Statistics about pattern detection

PatternMemory

Memory data for pattern analysis

SalienceEvent

A salience spike event

SemanticCluster

A detected semantic cluster

TemporalCluster

A detected temporal cluster (session)

Enums

BehaviorChangeType

Type of behavioral change detected

ReplayTrigger

Trigger types for pattern-based replay