| DIGS Path,Content | |
| Literal Summary,"Design to simulate human brain neural pathways using spreadsheets with mathematical memory segmentation, similarity routing, entropy tracking, and agent interaction maps." | |
| Conceptual Extraction,"Model cognitive processes such as pathway reinforcement, similarity-based recall, and decay resistance using spreadsheet tables and formulas. Agents interact with segmented knowledge via weighted recall maps, mimicking synaptic strength and neuroplasticity." | |
| Terminology Indexing,"['SegmentID', 'Entropy', 'Recall Strength', 'Linked Segments', 'Phi Score', 'Luhn Checksum', 'AgentID', 'Tags', 'Activation History', 'Fibonacci Indexing', 'Similarity Threshold', 'BaseEntropy']" | |
| Structural Mapping,"{'Files': ['SegmentMemory.csv', 'AgentMemoryMap.csv', 'MemoryInsertionQueue.csv', 'NeuroRoutingLog.csv'], 'Relations': {'Agents ↔ Segments': 'Via weighted mappings', 'Segments ↔ Segments': 'Via Linked Segments column', 'Memory Insertions': 'Routed through existing memory prior to being accepted'}}" | |
| Data Analysis,"{'Entropy Formula': 'Entropy = BaseEntropy * exp(-UsageScore * phi)', 'Recall Update': 'RecallStrength = PriorStrength + (0.1 × ActivationRelevance)', 'Similarity Matching': 'Based on content tags and historical activation patterns'}" | |
| Comparative Analysis,"{'Neuroscience Parallel': 'Simulates Hebbian learning, memory reinforcement, synaptic decay', 'Technical Edge': 'Eliminates 2,000 character limits by using distributed, evolving memory links'}" | |
| Practical Application,"{'Use Case': 'Long-term agentic AI memory system with spreadsheet-based recall logic', 'Tools': ['Python', 'Google Sheets', 'Excel formulas', 'CSV routing middleware'], 'Outcome': 'Synthetic cognition structure with mathematically managed growth'}" | |
| Cross-Referencing,"{'Related Topics': ['Neuroplasticity', 'Hebbian Learning', 'Cognitive Graphs', 'Memory Mapping'], 'Future Extensions': ['Visual pathway maps', 'Agent evolution tracking', 'Memory decay visualization', 'Agent-triggered schema reinforcement']}" | |
| Structural Mapping,"Added ShortTermMemory.csv (active agent memory), SegmentHistory.csv (memory usage tracking), and SegmentConflictMap.csv (conflict arbitration map). Each memory type represents a functional layer similar to RAM, LTM, and contradiction gatekeepers." | |
| Data Analysis,"Integrated trust score (-1.0 to +1.0), conflict detection thresholds, entropy decay curves, and agent-specific relevance scores. Memory retention is now based on usage, truth confidence, and contextual fit." | |
| Comparative Analysis,"Memory behavior now mirrors human neural filtration: subconscious filtering, relevance scoring, and short-term recall activation. Contradictions and redundancies are challenged at insertion with preference given to historically validated knowledge." | |
| Practical Application,"Agents now evaluate memory segments before loading or referencing, allowing memory to behave like cognitive schema selectors. Memory growth, trust arbitration, and decay processes mirror subconscious/conscious layer interactions." | |
| Cross-Referencing,"Future development to include subconscious archetype modeling, contradiction propagation resistance, and recursive reinforcement learning via segment-to-agent evolutionary logs." | |