Unconventional Computing Laboratory, University of the West of England, Bristol, UK; Mayne Bio Analytics Ltd., Cinderford, UK.
Unconventional Computing Laboratory, University of the West of England, Bristol, UK; Advanced Machinery and Productivity Institute, University of Huddersfield, Huddersfield, UK.
Biosystems. 2023 Jul;229:104933. doi: 10.1016/j.biosystems.2023.104933. Epub 2023 May 29.
Living fungal mycelium networks are proven to have properties of memristors, capacitors and various sensors. To further progress our designs in fungal electronics we need to evaluate how electrical signals can be propagated through mycelium networks. We investigate the ability of mycelium-bound composites to convey electrical signals, thereby enabling the transmission of frequency-modulated information. Mycelium networks were found to reliably transfer signals with a recoverable frequency comparable to the input, in the 100Hz to 10 000Hz frequency range. Mycelial adaptive responses, such as tissue repair, may result in fragile connections, however. While the mean amplitude of output signals was not reproducible among replicate experiments exposed to the same input frequency, the variance across groups was highly consistent. Our work is supported by NARX modelling through which an approximate transfer function was derived. These findings advance the state of the art of using mycelium-bound composites in analogue electronics and unconventional computing.
活体真菌菌丝网络已被证明具有忆阻器、电容器和各种传感器的特性。为了在真菌电子学的设计上取得进一步进展，我们需要评估电信号如何在菌丝网络中传播。我们研究了菌丝结合复合材料传递电信号的能力，从而能够传输调频信息。研究发现，在 100Hz 至 10 000Hz 的频率范围内，菌丝网络能够可靠地传输与输入频率可恢复的信号。然而，菌丝体的自适应反应，如组织修复，可能导致连接脆弱。虽然暴露于相同输入频率的重复实验中输出信号的平均幅度不可重复，但组间的方差高度一致。我们的工作得到了 NARX 建模的支持，通过该模型推导出了一个近似的传递函数。这些发现推动了在模拟电子学和非常规计算中使用菌丝结合复合材料的技术发展。
