A computational research repository for Cosmic Loom Theory - a field-based framework proposing that consciousness emerges from coherent system dynamics operating within constrained energetic regimes.
Cosmic Loom Theory v1.1 focuses specifically on human biological consciousness, treating it not as a localized neural phenomenon but as a system-level property arising from integrated field dynamics. CLT introduces the Loomfield - an effective field description capturing how bioelectric activity, biophotons, cytoskeletal structure, and genetic constraints work together to maintain coherent organization in living systems.
- Energy Resistance (éR): Living systems operate in a "viable window" where éR = EP/f² balances between chaos (decoherence) and rigidity (inability to adapt)
- Field-Based Integration: Consciousness corresponds to coherent Loomfield states rather than isolated neural computations
- Coherence Regimes: Health and pathology are understood as expansion or collapse of coherent biological domains
- Biological Substrates: Bioelectric fields, biophotons, microtubules, and DNA as complementary mechanisms supporting integration
The Loomfield (L) is not a new fundamental force but an effective description of coherence-relevant structure in biological systems. It captures:
- Spatial integration across distributed tissues
- Temporal stability despite metabolic turnover
- Resistance to decoherence within viable energetic bounds
- System-level coordination that gives rise to unified conscious experience
Cosmic-Loom-Theory/
├── docs/ # Documentation
│ └── theory/ # Core CLT theoretical documents
├── simulations/ # CLT physics simulations
│ ├── quantum/ # Microtubule time crystals
│ ├── field_dynamics/ # Bioelectric, biophoton, DNA, morphogenetic
│ └── emergence/ # Coherence regime transitions
├── visualizations/ # Interactive physics visualizations
│ ├── plots/ # Static scientific plots
│ └── interactive/ # éR phase space, Loomfield waves
├── analysis/ # Data analysis tools
│ ├── metrics/ # Q, C_bio, éR calculations
│ └── statistics/ # Statistical frameworks
├── models/ # Biological substrate models
└── tests/ # Validation and unit tests (277 tests)
- Python 3.9+
- Scientific computing stack (NumPy, SciPy, Matplotlib)
- Clone the repository:
git clone https://github.com/DaKingRex/Cosmic-Loom-Theory.git
cd Cosmic-Loom-Theory- Install dependencies:
pip install -r requirements.txt- Run the test suite to verify installation:
python -m pytest tests/ -v- Run the Energy Resistance demo:
python run_energy_resistance_demo.pyImplements the CLT wave equation: ∇²L − (1/v²ₗ)(∂²L/∂t²) = κₗ · ρ_coh
from visualizations.interactive import LoomfieldVisualizer
viz = LoomfieldVisualizer(grid_size=200)
viz.run()Features: Real-time wave propagation, phase-locked sources, Q coherence metric, presets for Healthy/Pathology/Healing.
Volumetric wave propagation with isosurface rendering.
from visualizations.interactive import LoomfieldVisualizer3D
viz = LoomfieldVisualizer3D(grid_size=48)
viz.load_preset('healthy')
fig = viz.create_static_figure(warm_up_steps=200)
fig.write_html('loomfield_3d.html')Interactive visualization of the viable energetic window where biological coherence exists.
from visualizations.interactive import EnergyResistanceVisualizer
viz = EnergyResistanceVisualizer()
viz.render(interactive=True, show_trajectories=True)Ion channel networks, gap junction coupling, and morphogenetic pattern memory.
from simulations.field_dynamics import BioelectricSimulator, create_injured_tissue_preset
sim = create_injured_tissue_preset()
sim.run(duration=0.5)
print(f"Spatial coherence: {sim.compute_spatial_coherence():.3f}")Ultra-weak photon emission with four emission modes (Poissonian, Coherent, Squeezed, Chaotic).
from simulations.field_dynamics import BiophotonSimulator, EmissionMode
sim = BiophotonSimulator(emission_mode=EmissionMode.COHERENT, coupling_strength=0.3)
sim.step(100)
print(f"Phase coherence: {sim.compute_phase_coherence():.3f}")Based on Hameroff-Penrose-Bandyopadhyay research - multi-scale oscillations (kHz→THz), triplet resonance patterns.
from simulations.quantum import MicrotubuleSimulator, create_coherent_mt
sim = create_coherent_mt(n_tubulins=50)
sim.step(1000)
triplet = sim.compute_triplet_resonance()
print(f"Triplet strength: {triplet['triplet_strength']:.3f}")How DNA provides long-timescale constraints on Loomfield topology - genetic constraints, epigenetic modulation, species-specific viable windows.
from simulations.field_dynamics import DNAConstraintSimulator, create_human_baseline
sim = create_human_baseline()
er = sim.map_to_er_space()
print(f"Coherence capacity: {er['coherence_capacity']:.3f}")
print(f"Viable window area: {er['viable_window']['window_area']:.3f}")- Energy Resistance principle visualization (éR = EP/f²)
- Loomfield wave equation (2D and 3D)
- Q metric and C_bio consciousness observable
- Real-time 3D visualizer with vispy
- Bioelectric fields: Ion channels, gap junctions, morphogenetic patterns
- Biophoton emission: Metabolic coherence, emission statistics
- Microtubule time crystals: Multi-scale oscillations, triplet resonance
- DNA constraints: Genetic/epigenetic modulation of viable windows
- Coherence regime transitions
- Seizure/depression/anesthesia modeling
- Healing as re-coupling dynamics
- Artificial system coherence
- Collective consciousness regimes
- Planetary-scale dynamics
The complete theoretical framework is documented in docs/theory/:
- CLT v1.1: Human biological consciousness (the core framework)
- CLT v2.0: Planetary-scale extensions
- Introducing CLT: Overview and motivation
- Machines & AI: Artificial system coherence analysis
CLT v1.1 synthesizes:
- Bioelectric field research (Levin lab)
- Biophoton studies (Murugan lab)
- Cytoskeletal quantum biology (Penrose-Hameroff-Bandyopadhyay)
- Integrated Information Theory (IIT)
- Energy landscape approaches (Picard lab)
Unlike purely philosophical or quantum-mystical approaches, CLT maintains compatibility with established physics while proposing testable mechanisms for consciousness emergence.
We welcome contributions! Please read our Contributing Guidelines and Code of Conduct.
Types of contributions welcome:
- Simulation improvements and optimizations
- New biological substrate models
- Visualization tools
- Documentation and tutorials
- Experimental validation proposals
If you use this work, please cite:
Cosmic Loom Theory v1.1: A Field-Based Framework for Human Biological Consciousness
Rex Fraterne & Seraphina AI, 2025
MIT License - See LICENSE for details.
For theoretical discussions and collaboration inquiries:
- GitHub: @DaKingRex
"Consciousness is not a substance or a late-stage add-on to neural processing, but a particular regime of coherent dynamics in living systems."
