Bioelectrical Dynamics and Emergent Computing Trajectories of Trichoderma on Rice Substrates

Abstract

We report the bioelectrical properties of T. reesei grown on rice within a living composite of ferrous oxide, calcium nitrate, and alginate. Scanning electron microscopy (SEM), cyclic voltammetry, and long-duration electrical recordings were performed. Seven of twelve working electrodes (Ch1–Ch4, Ch6–Ch8; 0–14 cm) yielded analyzable spike trains. SEM revealed a porous layered scaffold containing fungal hyphae, iron oxide, calcium salts, and sub-micrometer particles, consistent with fungal-wall-guided biomineralization. Cyclic voltammetry showed quasi-reversible faradaic behavior stabilizing after 25–30 cycles, with persistent pinched-hysteresis memristive features. Electrical recordings over $$  s showed organized, non-stationary oscillations. Ch5 was excluded due to electrode artifact; Ch9–Ch12 showed no detectable activity. Spike counts ranged from 102 to 6626 events; mean amplitudes from 0.084 to 4.494 mV. Encoding activity as a seven-bit binary state vector yielded a spontaneous structured trajectory exploring only a small fraction of the theoretical $$ state space, exhibiting finite-state-machine-like organization without defined inputs or outputs. NDR reached $$  M $$ at $$  V. The composite thus exhibited memristive and self-organizing electrical behavior, with dynamics consistent with reservoir computing principles, neuromorphic signal routing, and spontaneous finite-state computation.