Digital Twin of Mycelium From Hardware Environment Aware Growth Models to Mycelium-Based Computing
The work presents a hardware-based digital twin framework for mycelium growth modelling and biohybrid computing applications, developed within the FUNGATERIA project. It demonstrates the parallel implementation of nine environmental-aware mycelium growth models on FPGA hardware, incorporating real-time environmental sensing of temperature, humidity, and light. Furthermore, it explores mycelium-inspired reservoir computing architectures and their application to neurological signal processing, including Parkinson's disease recognition from EEG signals.
Authors
- Georgios Ch. Sirakoulis (ORCID: https://orcid.org/0000-0001-8240-484X)
- G. Kleitsiotis (ORCID: https://orcid.org/0000-0002-4782-6390)
- Iosif-Angelos Fyrigos (ORCID: https://orcid.org/0000-0001-8032-1725)
- Ioannis K. Chatzipaschalis (ORCID: https://orcid.org/0000-0002-3468-4442)
- Theodoros Panagiotis Chatzinikolaou (ORCID: https://orcid.org/0000-0002-2587-1895)
- Ioannis Tompris (ORCID: https://orcid.org/0009-0002-7189-9896)
Institutions
- Democritus University of Thrace (GR)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-26
- DOI
- https://doi.org/10.5281/zenodo.23669682
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission