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']}"