Mycoponically Integrated Network Device for Multimodal Sensing with Living Mycelial Networks

2026-04-24

AI Summary

This study addresses the limitations of conventional multimodal environmental monitoring systems, which rely on specialized sensors and struggle to adapt to unknown or dynamic stimuli, varying electrode configurations, and self-repair requirements. The work proposes a universal biosensing platform by integrating living fungal mycelial networks into a porous bioceramic substrate. Without any hardware modifications, the system identifies 14 distinct stimulus types—including chemical, optical, mechanical, thermal, and biological—leveraging a Hill-type calibration model and a multi-channel electrophysiological signal decoding algorithm to reconstruct stimulus duration, spatial origin, and continuous location. The platform exhibits cross-species conserved responsiveness, tunable sensitivity, autonomous recovery within 72 hours post-damage, and sustained stable operation for over 11 months on a single device.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Multimodal environmental monitoring conventionally requires a suite of purpose-built transducers, each constrained to a predefined target. Here, we present MIND (Mycoponically Integrated Network Device), a platform that sustains living fungal mycelial networks on porous bioceramic substrates and reads their passive extracellular voltages. Without hardware modification, a single device produces distinguishable bioelectrical responses to 14 stimuli spanning chemical, optical, mechanical, thermal, and biological domains. We show that steady-state intensity responses follow Hill-type calibration functions conserved across five phylogenetically diverse fungal species, and that multichannel decoding recovers stimulus duration, spatial origin, and continuous position from the bioelectrical output. Strain selection tunes sensitivity without hardware redesign. The platform restores full electrophysiological function within 72 h of mechanical damage and has maintained calibration-quality readout for more than 11 months of continuous operation. These results position fungal electrophysiology as a measurement platform for sensing applications in which the full stimulus set, the electrode geometry, and the recovery requirements cannot be fully specified in advance.

Problem

• multimodal sensing
• environmental monitoring
• biosensors
• fungal mycelial networks
• adaptive sensing

Innovation

• mycelial networks
• multimodal sensing
• bioelectrical response
• self-healing biosensor
• Hill-type calibration