Digital Twins for Fungal Computing: Viable XOR Regimes, Parameter Inference, and Waveform-Guided Rediscovery

Fungal substrates are promising candidates for unconventional computing, but specimen-to-specimen variability makes logic-gate fabrication difficult to reproduce. This paper presents a digital-twin workflow for fungal excitable networks and evaluates three components needed for computeraided design: identifying parameter regimes that support XOR computation, inferring latent biophysical parameters from electrical characterization data, and refining those inferred parameters by waveform matching. The model represents mycelium as a random geometric graph with FitzHugh-Nagumo node dynamics and memristive edge conductances. A systematic optimization study over 160 simulated specimens identifies a viable XOR subspace defined by tuned biophysical parameters, electrode geometry, and stimulus timing. A characterization study over 400 simulated specimens uses step-response, paired-pulse, and triangle-sweep protocols to extract 94 response features. Random forest regressors recover several latent parameters reliably (R2 = 0.912 for{tau} v, 0.816 for{tau} w, 0.717 for a), while vscale, Ron, and Roff remain weakly identifiable. On a preliminary rediscovery validation using 15 optimized specimens (20-50 nodes), ML initialization followed by local waveform-matching refinement reduces mean waveform mismatch from 1.070 to 0.042 (96.0%; one-sided Wilcoxon p = 3.1 x 10-5) and reduces mean core-parameter error from 16.6% to 8.8% (p = 6.1 x 10-5). A sensitivity analysis on 72 viable specimens reveals that{tau} w and are the most consequential parameters for XOR twin accuracy, while vscale and Roff are both hard to identify and tolerant to error. These results show that fungal digital twins can already narrow the search for viable computational substrates, partially recover the excitable dynamics that govern them, and support small-scale specimen-specific refinement without yet claiming full XOR transfer.