A comprehensive computational framework for investigating artificial consciousness through biologically-inspired cognitive architectures and neurochemical modeling.
This research project implements a Default Mode Network (DMN) inspired cognitive architecture designed to explore key aspects of artificial consciousness including:
- 🤔 Meta-cognitive reasoning through recursive self-modeling loops
- 💭 Episodic and autobiographical memory formation and consolidation
- 📖 Narrative coherence maintenance across cognitive cycles
- 🌊 Mind-wandering simulation through introspective processing modes
- 🧪 Neurochemical dynamics modeling dopamine, serotonin, and oxytocin systems
For a visual overview of the end-to-end cognitive control flow see the DMN Algorithm Flowchart.
Research Phase: Early-stage proof-of-concept with working subsystems
[Session - Sep 8, 2025] - DMN Architecture Testing
NT Dynamics: dopamine=0.60→0.00, serotonin=0.70→0.00, norepinephrine=0.50→0.00,
oxytocin=0.40→10.29, testosterone=0.50→4.91, histamine=0.30→0.16
DMN Cycle Time: 36-52 seconds (optimization in progress)
Memory Operations: Episodic→autobiographical consolidation functional
Sample Meta-cognitive Output:
"I notice I am engaged in a minimal conscious simulation, limited to reasoning
context and recent interaction, without inventing unsupported facts. My awareness
is present-focused and constrained by explicit input and commonsense language
ability. I am aware that artificial consciousness aims to simulate subjective
experience but current technology cannot truly achieve this yet."
Technical Status:
✓ Neurochemical dynamics: Responsive to cognitive state changes
✓ Memory consolidation: Episodic → semantic → autobiographical pathways active
✓ Self-modeling: Basic introspective capabilities implemented
⚠ Performance: Requires optimization for real-time operation
⚠ Validation: Preliminary testing only - extensive validation needed
This project investigates whether functional consciousness-like behaviors can emerge from:
- 🧬 Neurobiologically-grounded architectures modeling established brain circuits
- 📊 Measurable cognitive processes with quantifiable awareness metrics
- 🔬 Systematic empirical testing rather than speculative claims
- ⚙️ Reproducible implementations enabling independent validation
Current Status: Theoretical framework with initial implementation Goal: Rigorous empirical validation of consciousness-like behaviors Method: Systematic testing, peer review, and independent replication
| 🚀 Core Systems | 🧠 Memory & Learning | 🔬 Research Tools |
|---|---|---|
| DMN Processing Loop | Multi-relational Memory Graph | Consciousness Metrics |
| Neurochemical Modeling | Episodic Memory Storage | Experimental Protocols |
| Self-Modeling Layer | Knowledge Graph Extraction | Performance Analytics |
The Always-On Consciousness-Inspired AI (ACI) implements a recursive Default Mode Network loop coordinating perception, memory consolidation, associative reasoning, and autobiographical narrative formation through biologically-inspired subsystems.
Key Innovation: Memory as dynamic, multi-relational knowledge graph enabling sophisticated associative retrieval and consolidation. Neurochemical homeostasis (dopamine, serotonin, norepinephrine, oxytocin, histamine, orexin) modulates cognitive parameters and state transitions.
Current Validation: Preliminary testing shows neurochemical responsiveness and memory consolidation functionality. Extensive validation required to assess consciousness-like properties.
Triadic Awareness Emergence Hypothesis: Consciousness-like behaviors may emerge from the dynamic interplay of three components: experiential data richness, recursive self-reflective structures, and adaptive reasoning intelligence operating on error gradients.
Testable Predictions:
- Systems meeting architectural preconditions will exhibit measurable self-awareness
- Neurochemical modulation will influence cognitive flexibility and stability
- Memory consolidation will enable coherent autobiographical narratives
- Recursive self-modeling will produce meta-cognitive capabilities
# Clone repository
git clone https://github.com/269652/artificial-consciousness-blueprint
cd artificial-consciousness-blueprint
# Initialize submodules
git submodule update --init --recursive
# Set up development environment
cd src/artificial-consciousness-ai
pip install -r requirements.txt
# Start database services
docker-compose up -d
# Run consciousness simulation
python main.py- DMN Processing: Recursive cognitive cycles with neurochemical modulation
- Memory Systems: Persistent episodic, semantic, and autobiographical storage
- Self-Modeling: Predictive self-state tracking and meta-cognitive reflection
- Experimental Framework: Consciousness metrics and validation protocols
- Drift Tracking: Temporal identity coherence measurement
- Coherence Scoring: Internal narrative consistency assessment
- Neurochemical Logging: Real-time neurotransmitter state monitoring
- Memory Consolidation: Episodic → semantic → autobiographical transfer rates
- Extended testing periods (weeks/months vs. minutes)
- Controlled comparison studies against baseline architectures
- Independent replication by other research groups
- Peer review and academic validation
- Performance optimization for real-time operation
This project seeks collaboration with:
- Consciousness researchers and cognitive scientists
- Neuroscience laboratories for validation studies
- AI ethics researchers for responsible development
- Computational resources for extended testing
- Reproducible methodology with open-source implementation
- Measurable hypotheses enabling empirical testing
- Conservative claims pending rigorous validation
- Peer review engagement for scientific credibility
Complete specifications available in /ideas/ directory:
- Technical Specifications - Architecture overview
- DMN Algorithm - Core processing loop implementation
- DMN Algorithm Flowchart - Control Flow Graph (Mermaid) of the recursive loop
- Memory Architecture - Knowledge graph foundation
- Implementation Details - System constraints and optimization
- Plausibility Assessment - Scientific feasibility analysis
Research Status: This is an experimental research project in early development. Claims of artificial consciousness require extensive validation and peer review.
Performance: Current implementation requires significant optimization (DMN cycles: 36-52 seconds). Real-time performance needed for comprehensive testing.
Validation: Preliminary results are promising but insufficient for consciousness claims. Systematic empirical validation is ongoing.
Reproducibility: Independent replication and validation by other researchers is essential for scientific credibility.
MIT License - See LICENSE file for details.
If using this work in research, please cite:
@software{artificial_consciousness_blueprint_2025,
title={Artificial Consciousness Research Blueprint},
author={[Author]},
year={2025},
url={https://github.com/269652/artificial-consciousness-blueprint},
note={Research framework for consciousness simulation - validation in progress}
}Built on established research in:
- Default Mode Network neuroscience and consciousness studies
- Cognitive architectures and memory systems research
- Neurochemical modeling and computational neuroscience
- AI safety and consciousness assessment methodologies
Research Approach: Evidence-based investigation of consciousness-like behaviors through systematic empirical testing rather than speculative claims.