Memristor-Based Digital Twin of Mycelium for Unconventional Computing
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Fungal mycelium is widespread in nature and stands out among Engineered Living Materials (ELMs). When stimulated, it shows intricate electrical signaling activity that with the help of digital twins can be used for sustainable computing. For its further study, its emulation with novel hardware is important. Because of their special state-transition properties and non-volatility, memristors show promise for creating an effective, low-power replica of mycelium. This paper focuses on how RRAMs can be utilized to form a flexible spiking reservoir network as an alternative of common spiking neural networks (SNNs). It serves as a novel computational primitive that mimic the adaptive, self-organizing characteristics of mycelium, capable for unconventional computing.