Fungal-Syntactic-Information-Processing

Computational analysis of syntactic information processing in fungal electrical networks using information theory, grammar induction, and 137-hour electrophysiology datasets.

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Syntactic Information Processing in Fungal Electrical Networks

DOI License: MIT

Computational analysis and modeling framework for testing whether fungal mycelial networks exhibit grammar-like syntactic information encoding.

📄 Paper

"Syntactic Information Processing in Fungal Electrical Networks: Evidence from Schizophyllum commune and Computational Modeling"

🧬 Research Question

Do biological networks encode information using grammar-like syntactic rules (analogous to quantum error correction) rather than simple Boolean logic or random processes?

🔬 Key Findings

  • 90.2% compressible patterns (Kolmogorov complexity = 0.098) in 137 hours of Schizophyllum commune electrical recordings
  • Shannon entropy: 4.84 bits (10s windows), 51.6% redundancy
  • Markov dissociation: Timing follows simple rules (p=0.560), but content is super-compressible (p=0.007)
  • 419× information capacity advantage for syntactic error correction vs Boolean logic in computational models

🚀 Quick Start

Installation

git clone https://github.com/zubairchowdhury888-art/Fungal-Syntactic-Information-Processing.git cd Fungal-Syntactic-Information-Processing pip install -r requirements.txt

Basic Usage

  1. Spike Detection from src.spike_detection import detect_spikes spikes = detect_spikes('data/sample_spike_window.csv', threshold=-0.15, min_separation=30)

  2. Information Theory Analysis from src.entropy_analysis import calculate_entropy, kolmogorov_complexity H = calculate_entropy(spikes, window_size=10) K = kolmogorov_complexity(spikes)

  3. Markov Baseline Comparison from src.markov_baseline import generate_markov_sequences, compare_to_baseline markov_seqs = generate_markov_sequences(spikes, n=20) t_stat, p_value = compare_to_baseline(spikes, markov_seqs)

  4. Cellular Automaton Models from src.cellular_automaton import run_simulation results = run_simulation(model='syntactic', noise_level=0.15, trials=5)

📊 Reproducing Results

Complete analysis pipeline:

Run all analyses (generates results/ folder with CSVs and figures) python src/01_spike_detection.py python src/02_entropy_analysis.py python src/03_markov_baseline.py python src/04_cellular_automaton.py

Or use Jupyter notebooks for interactive exploration:

jupyter notebook notebooks/01_Data_Exploration.ipynb

📁 Data Availability

Small sample dataset (for testing code): data/sample_spike_window.csv

Full dataset (137 hours, 494,044 data points): https://zenodo.org/records/18111484

Includes:

  • Raw voltage time series (5 channels)
  • Detected spike times (395 events)
  • Processed analysis results
  • Cellular automaton simulation outputs

🧪 Experimental Protocol

Complete wet-lab replication protocol: docs/EXPERIMENTAL_PROTOCOL.md

Materials Needed:

  • Schizophyllum commune culture (ATCC 38548 or Utrecht H4-8)
  • Iridium-coated electrodes (Spes Medica)
  • 24-bit data logger (ADC-24 or equivalent)
  • 30°C incubator

Expected Results:

  • Shannon entropy: 4-5 bits (10s window)
  • Kolmogorov complexity: 0.08-0.12
  • Markov test: p > 0.05

🔮 Falsifiable Predictions

Four testable predictions for experimental validation: docs/FALSIFIABLE_PREDICTIONS.md

P1: Grammar Extraction (£0 cost, immediate)

  • Apply Sequitur/ADIOS algorithms to spike trains
  • Expected: Context-free grammar with nesting depth ≥2

P2: Anomalous Noise Resistance (£500, 3-6 months)

  • Inject electrical noise, measure information preservation
  • Expected: <50% degradation vs Markov baseline

P3: Non-Local Synchronization (£2,000, 6-12 months)

  • Multi-electrode recording at varying distances (1-50mm)
  • Expected: Correlation decay slower than diffusion (∝ t^0.5)

P4: Phylogenetic Scaling (£0, immediate)

  • Compare grammar depth across species using existing datasets
  • Expected: Positive correlation (r>0.5) with neural complexity

📚 Citation

If you use this code or framework in your research, please cite:

@article{chowdhury2025syntactic, title={Syntactic Information Processing in Fungal Electrical Networks: Evidence from Schizophyllum commune and Computational Modeling}, author={Chowdhury, Zubair E.}, year={2025}, publisher={Zenodo}, doi={10.5281/zenodo.18111484} }

🤝 Contributing

Contributions welcome! Particularly interested in:

  • Wet-lab experimental validation
  • Extensions to other fungal species
  • Alternative grammar induction algorithms
  • Topological analysis of mycelial networks

📧 Contact

Zubair E. Chowdhury

📜 License

Code: MIT License
Data & Paper: CC BY 4.0

🙏 Acknowledgments

  • Andrew Adamatzky (UWE Bristol) for pioneering fungal electrophysiology methods
  • Suresh Kumar S (CSIR) for theoretical discussions on cosmic computation and topological information structures

Built With

  • computational-biology
  • data-analysis
  • information-theory
  • jupyter-notebook
  • matplotlib
  • numpy
  • pandas
  • python
  • scientific-computing
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