NeuroCognitive Architecture (NCA) Data Flow¶
Overview¶
This document describes the data flow architecture within the NeuroCognitive Architecture (NCA) system. It outlines how information moves through the system's components, focusing on the interactions between memory tiers, cognitive processes, and LLM integration points. This architecture is designed to support the biologically-inspired cognitive functions while maintaining performance, scalability, and reliability.
Core Data Flow Principles¶
- Hierarchical Processing: Data flows through hierarchical processing stages, mimicking biological neural systems.
- Bidirectional Communication: Components communicate bidirectionally, allowing for feedback loops and recursive processing.
- Event-Driven Architecture: The system responds to both external stimuli and internal state changes.
- Asynchronous Processing: Non-blocking operations enable parallel processing of information.
- Stateful Interactions: The system maintains state across interactions, supporting continuous learning.
High-Level Data Flow Diagram¶
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ External │────▶│ NCA Core │────▶│ LLM │
│ Interfaces │◀────│ Processing │◀────│ Integration │
│ │ │ │ │ │
└─────────────────┘ └────────┬────────┘ └─────────────────┘
│
▼
┌─────────────────┐
│ │
│ Memory │
│ System │
│ │
└─────────────────┘
Detailed Component Interactions¶
1. Input Processing Flow¶
- External Input Reception
- User queries, system events, and environmental data enter through API endpoints or CLI interfaces
- Input is validated, sanitized, and normalized
-
Context information is attached (session data, timestamps, source identifiers)
-
Cognitive Preprocessing
- Input classification and prioritization
- Context enrichment from working memory
-
Attention mechanism filters and focuses processing resources
-
Memory Retrieval
- Query construction for relevant information
- Parallel retrieval from appropriate memory tiers
- Information consolidation and relevance scoring
2. Core Processing Flow¶
- Cognitive Processing
- Information integration from multiple sources
- Pattern recognition and matching
- Inference and reasoning processes
-
Emotional and health state influence on processing
-
Decision Making
- Option generation based on processed information
- Evaluation against goals, constraints, and health parameters
-
Selection of optimal response or action
-
LLM Integration
- Construction of context-rich prompts
- Transmission to appropriate LLM endpoint
- Response parsing and integration
3. Output Processing Flow¶
- Response Formulation
- Integration of LLM outputs with internal processing results
- Consistency checking against memory and constraints
-
Response formatting according to output channel requirements
-
Memory Update
- Working memory updates with new information
- Short-term memory consolidation
-
Long-term memory storage decisions based on importance and relevance
-
Health System Updates
- Resource consumption tracking
- Health parameter adjustments
- Homeostatic regulation processes
Memory Tier Data Flows¶
Working Memory Flow¶
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Active │────▶│ Attention │────▶│ Processing │
│ Information │ │ Mechanism │ │ Units │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
▲ │
│ │
│ ▼
┌─────────────────┐ ┌─────────────────┐
│ │ │ │
│ Short-term │◀──────────────────────────│ Output │
│ Memory │ │ Formation │
│ │ │ │
└─────────────────┘ └─────────────────┘
- Capacity: Limited to ~7±2 chunks of information
- Persistence: Maintained for duration of active processing
- Access Speed: Sub-millisecond retrieval times
- Update Frequency: Continuous during active processing
Short-Term Memory Flow¶
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Working │────▶│ Consolidation │────▶│ Indexing │
│ Memory │ │ Process │ │ & Storage │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
│
▼
┌─────────────────┐ ┌─────────────────┐
│ │ │ │
│ Long-term │◀──────────────────────────│ Importance │
│ Memory │ │ Evaluation │
│ │ │ │
└─────────────────┘ └─────────────────┘
- Capacity: Moderate, holding recent interactions and context
- Persistence: Hours to days, depending on importance
- Access Speed: Millisecond-range retrieval times
- Update Frequency: Regular intervals and significant events
Long-Term Memory Flow¶
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Short-term │────▶│ Deep │────▶│ Vector │
│ Memory │ │ Encoding │ │ Embedding │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
│
▼
┌─────────────────┐ ┌─────────────────┐
│ │ │ │
│ Retrieval │◀──────────────────────────│ Persistent │
│ Mechanism │ │ Storage │
│ │ │ │
└─────────────────┘ └─────────────────┘
- Capacity: Virtually unlimited
- Persistence: Indefinite with periodic reinforcement
- Access Speed: Milliseconds to seconds, depending on indexing
- Update Frequency: Scheduled consolidation processes
LLM Integration Data Flow¶
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Context │────▶│ Prompt │────▶│ LLM API │
│ Assembly │ │ Engineering │ │ Interface │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
▲ │
│ │
│ ▼
┌─────────────────┐ ┌─────────────────┐
│ │ │ │
│ Memory │ │ Response │
│ System │◀──────────────────────────│ Processing │
│ │ │ │
└─────────────────┘ └─────────────────┘
- Context Assembly
- Retrieval of relevant information from memory tiers
- Current state information inclusion
-
Goal and constraint specification
-
Prompt Engineering
- Dynamic prompt construction based on context
- System message configuration
-
Parameter optimization (temperature, top_p, etc.)
-
LLM API Interface
- Request transmission with appropriate authentication
- Streaming response handling
-
Error and fallback management
-
Response Processing
- Parsing and validation of LLM output
- Integration with internal knowledge
- Consistency verification
Health System Data Flow¶
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Resource │────▶│ Health │────▶│ Regulation │
│ Monitoring │ │ Parameters │ │ Mechanisms │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
│
▼
┌─────────────────┐ ┌─────────────────┐
│ │ │ │
│ Cognitive │◀──────────────────────────│ Behavioral │
│ Processing │ │ Adjustments │
│ │ │ │
└─────────────────┘ └─────────────────┘
- Resource Monitoring
- CPU, memory, and API usage tracking
- Response time and throughput measurement
-
Error rate and exception monitoring
-
Health Parameters
- Current values for all health metrics
- Historical trends and baselines
-
Threshold definitions and alerts
-
Regulation Mechanisms
- Homeostatic adjustment processes
- Resource allocation optimization
-
Recovery and maintenance procedures
-
Behavioral Adjustments
- Processing priority modifications
- Response complexity regulation
- Self-maintenance scheduling
Data Storage and Persistence¶
- In-Memory Data
- Working memory contents
- Active processing state
-
Temporary calculation results
-
Database Storage
- Short-term memory in fast-access databases (Redis, MongoDB)
- Long-term memory in vector databases (Pinecone, Weaviate)
-
System configuration and health records in relational databases
-
File Storage
- Large binary assets
- Backup and archive data
- Log files and diagnostic information
Error Handling and Recovery Flows¶
- Error Detection
- Input validation failures
- Processing exceptions
- External service failures
-
Resource exhaustion events
-
Error Response
- Graceful degradation pathways
- Fallback processing options
-
User communication strategies
-
Recovery Processes
- State restoration from persistent storage
- Incremental capability restoration
- Self-healing procedures
Security Considerations¶
- Data Protection
- Encryption of data in transit and at rest
- Access control for memory contents
-
Sanitization of inputs and outputs
-
Authentication and Authorization
- Identity verification for all external interactions
- Permission-based access to system capabilities
-
Audit logging of sensitive operations
-
Threat Mitigation
- Rate limiting and throttling
- Anomaly detection in usage patterns
- Isolation of processing environments
Performance Optimization¶
- Caching Strategies
- Frequently accessed memory items
- Common processing results
-
External API responses
-
Parallel Processing
- Multi-threaded memory retrieval
- Distributed cognitive processing
-
Asynchronous external service calls
-
Resource Management
- Dynamic allocation based on priority
- Preemptive scaling for anticipated load
- Garbage collection and cleanup processes
Monitoring and Observability¶
- Metrics Collection
- Component performance statistics
- Memory usage and access patterns
-
Health parameter values
-
Logging
- Structured logs with context information
- Error and exception details
-
Decision process tracing
-
Alerting
- Threshold-based notifications
- Anomaly detection alerts
- Predictive maintenance warnings
Conclusion¶
The NCA data flow architecture provides a comprehensive framework for information processing that mimics biological cognitive systems while leveraging modern distributed computing principles. This architecture supports the system's core capabilities of memory management, cognitive processing, and adaptive behavior while maintaining performance, security, and reliability.
The modular design allows for incremental implementation and testing of components, supporting the phased development approach outlined in the project roadmap. As the system evolves, this data flow architecture provides clear integration points for new capabilities and optimizations.