This repository is the official companion code for the paper:
Vladimir Khomyakov (2025). A Multi-Level Computational Model of Macro-Consciousness with Self-Organizing Inter-Cluster Networks, Predictive Adaptation, and Reproducible Python Simulations. Zenodo. https://doi.org/10.5281/zenodo.16937283
It provides a full implementation of the computational framework described in the paper, enabling fully reproducible simulations and interactive explorations of the model.
This work presents a hierarchical computational framework for modeling emergent macro-consciousness. The model is based on:
- Micro-Level: "Self" units that minimize entropy and adapt to their environment.
- Meso-Level: Clusters of units with self-organizing interconnections and memory.
- Macro-Level: A global cognitive projection that emerges from cluster dynamics and provides top-down feedback.
The framework integrates adaptive learning, predictive task alignment, and a Hebbian-like rule for self-organizing inter-cluster weights. Simulations demonstrate the emergence of macro-cognitive coherence, adaptive network reconfiguration, and the system's ability to align with external tasks.
macro_consciousness/
│
├── scripts/ # Main simulation scripts
│ ├── full_consciousness_interactive.py # Interactive sim with 3D network & sliders
│ ├── full_consciousness_3D.py # 3D network visualization animation
│ ├── full_consciousness_panel.py # Non-interactive run → full panel of plots
│ └── self_learning_consciousness_with_weight_dynamics.py # Core weight dynamics sim
│
├── figures/ # (Directory for saving output figures)
├── data/ # (Directory for saving output data)
├── LICENSE # MIT License
└── README.md # This file
To run the simulations, you need Python 3.9 or later. The required libraries can be installed via pip:
Create a virtual environment (recommended)
python -m venv consciousness-env
# On macOS/Linux:
source consciousness-env/bin/activate
# On Windows (PowerShell):
consciousness-env\Scripts\activatepip install numpy matplotlib networkxAll main scripts are in the scripts/ directory. Each focuses on a different aspect of visualization.
- For an interactive experience (recommended first step):
Launches a 3D network visualization with sliders to adjust key parameters (eta, alpha, beta) in real-time.
python scripts/full_consciousness_interactive.py- To view a pre-rendered 3D animation:
Runs a fixed simulation and animates the resulting 3D network dynamics.
python scripts/full_consciousness_3D.py- To generate a comprehensive panel of plots:
Runs the simulation and generates a multi-plot figure showing entropy, macro-dynamics, cluster states, task signals, weight dynamics, and correlations. This is best for analysis.
python scripts/full_consciousness_panel.py- To run the core simulation:
Runs the model and plots the essential dynamics (entropy, macro-projection, clusters, tasks, weights).
python scripts/self_learning_consciousness_with_weight_dynamics.pyRunning the scripts will generate visualizations of the following emergent phenomena:
Global Entropy Reduction: The system minimizes its total entropy over time, displaying self-organizing behavior.
Predictive Macro-Dynamics: The macro-projection vector aligns with and predicts the external task signal.
Cluster Synchronization: Meso-level clusters show coordinated dynamics and varying degrees of correlation with the macro-state.
Self-Learning Network Weights: Inter-cluster weights (W) evolve based on a Hebbian-like rule, forming a complex, adaptive network.
Spatial-Temporal Correlation: Heatmaps and line plots showing how clusters couple with the emerging macro-consciousness.
If you use this model or code in your research, please cite the original publication:
@misc{khomyakov_vladimir_2025_16937283,
author = {Khomyakov, Vladimir},
title = {A Multi-Level Computational Model of Macro-Consciousness with Self-Organizing Inter-Cluster Networks, Predictive Adaptation, and Reproducible Python Simulations},
month = aug,
year = 2025,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.16937283},
url = {https://doi.org/10.5281/zenodo.16937283}
}- Version-specific DOI: 10.5281/zenodo.16937283
- Concept DOI (latest version): 10.5281/zenodo.16937282
- Download PDF: Direct link to paper on Zenodo
Theoretical Foundation: This work is based on the principles of Subjective Physics by A. Kaminsky (DOI: 10.5281/zenodo.15098840).
Predecessor Model: Builds upon the "Minimal Model of Cognitive Projection" (DOI: 10.5281/zenodo.16888675).
Author's Website: https://digitalphysics.ru/
macro-consciousness modeling, self-organizing networks, cognitive projection, observer entropy, predictive adaptation, multi-level cognitive architecture, subjective physics, reproducible python simulations, inter-cluster coupling, information-theoretic learning
This project is licensed under the MIT License. See the LICENSE file for details.
This work is based on principles of Subjective Physics by A. Kaminsky and the minimal model of cognitive projection.
-
AGI-25 Conference (2025): https://agi-conf.org/2025/
Annual conference on Artificial General Intelligence, providing a broader context for discussions on cognition, consciousness, and theoretical models of mind. -
Panel Discussion: "Rethinking the Mind: The Ghost and the Machine" (August 22, 2025)
Recording available at https://youtu.be/m_MBDc0tghc.
This panel addressed key themes relevant to the study of macro-consciousness and subjective physics.