Decoding Fungi  is a research project that investigates the growth patterns and electrical signals of Fomes fomentarius, a living fungal organism. Using Generative Adversarial Networks (GANs), trained on synthetic data based on observations of fungal growth, the project generates visualizations of mycelium development based on continuous environmental variables. This approach explores whether AI can model complex biological systems and raises questions about the relationship between machine learning and scientific representation. The project translates and maps electrical signals emitted by the mycelium, exploring alternative forms of sensing and communication in living systems. It also examines how GANs process and interpret data in ways fundamentally different from human perception. Rather than perceiving images holistically, GANs operate through layers of mathematical abstractions, analyzing features such as gradients, edges, and luminosities. The exhibition includes video footage of GAN training, an interactive model simulating fungal growth, and projections mapping the mycelium's electrical activity.

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