Syntactic Information Processing in Fungal Electrical Networks: A Computational Framework

This dataset provides complete raw data, analysis code, and computational models supporting the manuscript "Syntactic Information Processing in Fungal Electrical Networks: Evidence from Schizophyllum commune and Computational Modeling" (Chowdhury, 2025). Research Question: Do fungal mycelial networks encode information using grammar-like syntactic rules analogous to quantum error correction, rather than simple Boolean logic or random processes? Contents: Raw Electrophysiological Data 137.2 hours (494,044 seconds) of continuous voltage recordings from Schizophyllum commune mycelium 5 differential electrode channels sampled at 1 Hz Voltage range: ±78 mV (24-bit ADC resolution) Files: paste.txt, fast_spike_window.csv Processed Spike Data 395 detected spike events from high-density activity window (59 minutes) Spike detection via derivative thresholding (dV/dt < -0.15 mV/s, 30s minimum separation) Binary spike trains and inter-spike interval distributions Files: spike_analysis_enhanced.csv, comprehensive_event_catalog.csv, spike_analysis_summary.csv Information Theory Analysis Results Shannon entropy at multiple time scales (5s, 10s, 30s, 60s windows): H = 4.84 bits (10s) Kolmogorov complexity: K = 0.098 (90.2% compressible) Redundancy: 51.6% Markov baseline comparisons: 20 synthetic sequences with t-test results (p=0.560 entropy, p=0.007 complexity) Files: week1-2_information_theory_analysis.csv, summary_statistics.csv, window_comparison_summary.csv Cellular Automaton Simulations Three 50×50 grid models: Random, Boolean logic, Syntactic error correction Noise injection experiments (0-30% bit-flip rates, 5% increments) 105 total simulation runs (3 models × 7 noise levels × 5 trials) Mutual information preservation metrics demonstrating 419× capacity advantage for syntactic model Files included in analysis package Complete Python Analysis Code Spike detection algorithms Entropy and complexity calculations (gzip-based K approximation) Markov chain simulation and statistical testing Cellular automaton implementations with noise injection Directory: COMPLETE_PYTHON_CODE_PACKAGE Visualization and Graphs Voltage traces with detected spikes Entropy vs window size plots Mutual information degradation curves ISI distributions Files: Analysis.dgraph, Fast_spiking_analysis.dgraph, Action-potential-like-spikes.dgraph, Symmetric_Spikes.dgraph Experimental Protocol and Predictions Complete wet-lab replication protocol for independent verification Four falsifiable predictions with cost estimates (£0-£2,000) Files: week5-6_experimental_protocol.txt, week5-6_falsifiable_predictions.csv Key Findings: Fungal spike patterns show high structural redundancy (90.2% compressible) suggesting grammar-like encoding Temporal ordering is Markovian (p=0.560) but content exhibits super-compressibility (p=0.007), indicating syntactic structure beyond simple transition rules Computational models demonstrate syntactic error correction provides 419× higher information capacity than Boolean logic under noise conditions Methods: Secondary analysis of Schizophyllum commune electrophysiology data (following Adamatzky 2023, Scientific Reports 13:12808) combined with information-theoretic quantification and cellular automaton modeling. License: CC BY 4.0 (Creative Commons Attribution 4.0 International)