Mattbusel / Mycelium-Based-AI-Integration

This repository explores the intersection of synthetic biology and artificial intelligence by leveraging mycelium as a bio-computational substrate. The project aims to give AI systems a "body" by integrating machine learning models with the natural properties of mycelium, such as signal conduction, adaptive growth, and environmental reactivity.

Mycelium-Based AI Integration

Giving Machine Learning a Body Through Nature

This repository explores the intersection of synthetic biology and machine learning by using mycelium — the root structure of fungi — as a living, bio-computational interface. The project aims to give AI systems a body by integrating ML models with the adaptive, reactive properties of mycelium, creating feedback systems that blur the line between living and artificial intelligence.

This diagram illustrates the layered design of the system:

Biological Layer: Mycelium interfaced with electrodes and environmental stimuli.

Interface Layer: Microcontrollers acquire data and interact with ML models.

Computation Layer: Models interpret signals, classify states, and generate feedback.

Output Layer: Results are exposed through APIs or visualized via HTML dashboards.

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Project Goals

• Develop a Bio-Computational Interface
  Leverage mycelium's natural ability to conduct signals and adapt to stimuli.

• Create Bio-Electronic Feedback Loops
  Use microcontrollers and sensors to enable two-way communication between silicon and biology.

• Train Adaptive ML Models
  Implement machine learning systems that interpret and influence biological growth and reactivity.

• Enable Real-World Applications
  Explore integration into robotics, environmental sensing, and adaptive materials.

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Current Progress

Research & Exploration

• Analyzed mycelium’s signal-conducting and self-repairing properties.
• Surveyed existing literature on bio-computing and neuromorphic interfaces.

Prototyping

• Phase 1: Cultivating and testing signal conduction in mycelium samples.
• Phase 2 (in progress): Interfacing with sensors and microcontrollers.

Machine Learning Models

• Initial supervised and unsupervised ML models are being tested to classify and adapt to mycelium behavior.

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Roadmap & Milestones

Phase	Timeline	Objectives
Phase 1	0–6 Months	Grow and analyze mycelium conductivity; design basic interfaces
Phase 2	6–18 Months	Integrate ML models with

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📁 File Structure

mycelium-ai-integration/
├── README.md
├── requirements.txt
├── .gitignore
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
│   └── exploration.ipynb
├── src/
│   ├── __init__.py
│   ├── model/
│   │   ├── train.py
│   │   ├── predict.py
│   │   └── utils.py
│   ├── bio_interface/
│   │   ├── sensor_control.py
│   │   └── mycelium_signals.py
│   ├── api/
│   │   └── model_api.py
├── html/
│   └── ML_Growth_Pattern.html
├── sql/
│   └── ml.sql
├── experiments/
│   └── self_repairing_ai.md
├── config/
│   └── settings.yaml
├── docker/
│   ├── Dockerfile
│   └── docker-compose.yml
└── docs/
    └── architecture_diagram.png