Voltage Spiking Synchronicity with Growth Transitions in Trichoderma reesei.

Biological networks exhibit computational properties that rival engineered systems, yet the mechanisms underlying distributed information processing in living organisms remain poorly understood. Here, we characterise the bioelectrical dynamics of Trichoderma reesei mycelial networks and examine whether these patterns are consistent with distributed information processing frameworks. Continuous electrophysiological recordings over 72,138 s revealed 281 distinct electrical events with amplitudes spanning -3.55 to +2.05 mV, embedded within a complex hyphal architecture containing 88,319 branch points at 1.45 × 109 points/m2. Spike train analysis identified 162 burst events with exponential length distribution (characteristic length Lc = 1.275 spikes) and temporal organization following renewal process dynamics with 5.96-s refractory periods, demonstrating structured rather than stochastic electrical communication. Spiking frequency exhibited exponential decay kinetics (τ = 10.53 h) correlating with developmental transitions from colonization to maintenance metabolism. Boolean logic analysis using amplitude- and interval-based input variables revealed sparse gate activity (AND 0.9%, OR 10.6%, XOR 9.7%), while finite state machine modelling identified five operational states with 79.8% silent state occupancy, consistent with an event-driven signalling architecture. Amplitude distributions revealed a 4.4:1 polarity bias favoring depolarization events, indicating asymmetric information processing mechanisms. Symbolic dynamics analysis, validated against shuffle and Poisson surrogate controls (n = 200 each), revealed structured temporal patterns significantly exceeding chance expectation at all 30 lags examined (p < 0.05), confirming non-random organisation of bioelectrical activity. These findings show that fungal bioelectrical activity exhibits structured temporal organisation that can be described using computational frameworks including Boolean logic, burst-mediated temporal coding, and finite state machine modelling. Our results contribute to the growing characterisation of information processing in non-neural biological systems and provide a foundation for bio-inspired computing architectures that leverage the computational principles of living networks.