Mycelium Electrical Behavior: From Morphological Modeling to Hardware Implementation

This work investigates the morphological and electrical behavior of mycelium by developing a comprehensive hardware framework that links bio-inspired modeling with real-time electronics. We propose a digital twin of mycelium, integrating reaction-diffusion cellular automata (CA) models to simulate fungal growth behaviors and substrate interaction. These CA simulations are enhanced with environmental parameters and optimized through genetic algorithms to match experimental morphological data. We present a systematic transition from general-purpose, GPU-accelerated simulation environments to fixed-point, resource-constrained FPGA implementations, maintaining fidelity while allowing real-time performance. Extracting the morphological structure of mycelium from FPGA, this study explores the use of memristor-based circuits to emulate the adaptive electrical activity observed in fungi. A memristive oscillating framework is developed, enabling hardware-level representation of spiking behaviors and plastic signal propagation in mycelial networks. The integration of biological feedback, real-time control, and non-linear electrical characteristics supports low-power, adaptive systems suitable for neuromorphic and bio-hybrid computing architectures. This research offers a scalable pathway from material modeling to hardware realization, advancing digital twin approaches for engineered living materials.