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Open Access

Peer-reviewed

Research Article

Sustainable memristors from shiitake mycelium for high-frequency
bioelectronics

  * John LaRocco ,
   
    Roles Writing - original draft, Writing - review & editing

    * E-mail: john.larocco@osumc.edu

    Affiliation Psychiatry and Behavioral Health, Wexner Medical
    Center, Ohio State University, Columbus, Ohio, United States of
    America

    ORCID logo https://orcid.org/0000-0003-2116-5720

    
  * Qudsia Tahmina,
   
    Roles Funding acquisition, Resources

    Affiliation College of Engineering, Ohio State University,
    Columbus, Ohio, United States of America

    
  * Ruben Petreaca,
   
    Roles Resources, Supervision

    Affiliation College of Arts and Sciences, Ohio State University,
    Columbus, Ohio, United States of America

    
  * John Simonis,
   
    Roles Data curation, Formal analysis, Investigation

    Affiliation College of Engineering, Ohio State University,
    Columbus, Ohio, United States of America

    
  * Justin Hill
   
    Roles Investigation, Methodology

    Affiliation College of Engineering, Ohio State University,
    Columbus, Ohio, United States of America

    

Sustainable memristors from shiitake mycelium for high-frequency
bioelectronics

  * John LaRocco, 
  * Qudsia Tahmina, 
  * Ruben Petreaca, 
  * John Simonis, 
  * Justin Hill

PLOS
x

  * Published: October 10, 2025
  * https://doi.org/10.1371/journal.pone.0328965
  * 

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Figures

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Abstract

Neuromorphic computing, inspired by the structure of the brain,
offers advantages in parallel processing, memory storage, and energy
efficiency. However, current semiconductor-based neuromorphic chips
require rare-earth materials and costly fabrication processes,
whereas neural organoids need complex bioreactor maintenance. In this
study, we explored shiitake (Lentinula edodes) fungi as a robust,
sustainable alternative, exploiting its adaptive electrical
signaling, which is akin to neuronal spiking. We demonstrate fungal
computing via mycelial networks interfaced with electrodes, showing
that fungal memristors can be grown, trained, and preserved through
dehydration, retaining functionality at frequencies up to 5.85 kHz,
with an accuracy of 90 +- 1%. Notably, shiitake has exhibited
radiation resistance, suggesting its viability for aerospace
applications. Our findings show that fungal computers can provide
scalable, eco-friendly platforms for neuromorphic tasks, bridging
bioelectronics and unconventional computing.

Citation: LaRocco J, Tahmina Q, Petreaca R, Simonis J, Hill J (2025)
Sustainable memristors from shiitake mycelium for high-frequency
bioelectronics. PLoS One 20(10): e0328965. https://doi.org/10.1371/
journal.pone.0328965

Editor: Ye Zhou, Shenzhen University, HONG KONG

Received: July 8, 2025; Accepted: September 25, 2025; Published: 
October 10, 2025

Copyright: (c) 2025 LaRocco et al. This is an open access article
distributed under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source
are credited.

Data Availability: The data is available at this repository: https://
github.com/javeharron/abhothData.

Funding: Authors J.S. and J.H. were supported by Honda Research
Institute (grant AWD-118684). The funders had no role in study
design, data collection and analysis, decision to publish, or
preparation of the manuscript.

Competing interests: The authors have declared that no competing
interests exist.

Background

Overview

The development of neuromorphic hardware relies on memristive devices
capable of emulating synaptic behavior, with potential applications
in energy-efficient computing and artificial intelligence^1. Recent
work has explored natural, biodegradable substrates as sustainable
alternatives to conventional inorganic memristors [1]. In this study,
we investigated the potential of the edible fungus Lentinula edodes
(shiitake mushroom) as a platform for memristor fabrication. By
examining the electrical response of mushroom-derived materials under
repeated voltage cycling, we explored stable memristive switching
behavior, retention, and endurance. Shiitake-based devices not only
demonstrate reproducible memory effects, but also highlight the
potential for scalable, low-cost, and environmentally friendly
neuromorphic components.

Memristors

Memristor devices offer substantial advantages in robotic,
industrial, and transport applications due to their unique electrical
properties and ability to mimic neural functions. They can enhance
various control systems, facilitate efficient information processing,
and ultimately improve the overall performance of autonomous systems.

One of the key strengths of memristors is their capacity for
efficient and self-adaptive in situ learning, which is critical for
applications in robotics and autonomous vehicles. In memristor-based
neural networks, the devices can adjust their resistance based on
previous inputs, allowing for a form of analog learning that closely
resembles the synaptic behavior in biological systems [1]. This
capability enables robots and autonomous vehicles to learn from their
environment and adapt in real time, enhancing their ability to
navigate complex situations effectively. It has been found that such
systems can achieve low-latency responses, which are essential for
high-speed decision-making in dynamic environments [2].

Memristors also have the advantage of integrating memory and
processing capabilities into a single device, enabling a simplified
architecture for autonomous control systems [3]. For instance, in
autonomous vehicles, trajectory-tracking and path-following tasks can
be performed using memristor-based controllers that allow for rapid
calculations and real-time adjustments to control variables [4]. This
integration, especially with parallelization, helps to address the
challenges posed by separate memory and processing units, which can
lead to delays and increased power consumption in traditional control
systems [4].

Additionally, the resilience of memristor devices against
environmental changes, and their ability to operate under varying
conditions, make them particularly suitable for autonomous
applications, such as spacecraft electronics or vehicles operating in
unpredictable road environments [4]. This is complemented by the
precision in control that memristor-based systems can offer, which is
significant for maintaining stability and performance while following
desired trajectories [5].

Moreover, the low power consumption of memristors is particularly
beneficial in robotics and autonomous vehicles, where energy
efficiency is paramount. Hybrid analog-digital memristor systems can
minimize power usage during processing without sacrificing
responsiveness, which can prolong operational times by reducing the
frequency at which recharging or battery replacement is required,
enhancing the feasibility of deploying such systems in mobile
applications [2].

Ultimately, the potential of memristors to emulate human-like
decision-making and learning processes could be exploited to endow
robotic systems and autonomous vehicles with functionalities not
found in conventional control systems. The ability of memristors to
perform complex computations efficiently, learn adaptively, and
integrate both memory and processing into a unified approach make
them a cornerstone technology for the future development of
intelligent autonomous systems. However, the production of memristors
often requires rare-earth minerals and expensive semiconductor
foundries.

Fungal electronics

Fungi possess innate abilities to adapt to various environmental
conditions and efficiently process information through their
interconnected network of hyphae. These characteristics make fungi an
ideal candidate for developing sustainable computing systems from.
Our aim was to design and implement a novel fungal memristor-based
computing architecture that could significantly reduce energy
consumption and minimize electronic waste. We approached this using
substantially simpler bioreactors and nutrient cultures than those
required for conventional neurons and neural organoids. The unique
advantages of fungal memristors stem from the biological properties
of fungal materials, which distinguish them from typical inorganic or
polymer alternatives [6,7].

First, one of the main benefits of fungal memristors is their
environmentally sustainable and biodegradable nature. Conventional
memristors often contain transition metal oxides or silicon-based
structures, the production or disposal of which can pose
environmental challenges [6,7]. By contrast, fungal materials are
derived from organic biomass, making them both sustainable and
significantly less harmful to the environment. This aligns with
increasing efforts toward developing greener electronic materials, as
highlighted in previous work emphasizing the importance of
sustainability in technology development [8].

Second, fungal memristors exhibit remarkable adaptability in their
electrical properties. The structural composition of fungal materials
often allows for a range of conductive pathways that can form
dynamically under the influence of electrical stimuli, similar to the
conductive filaments formed in conventional memristors [9,10]. This
adaptability can lead to enhanced performance in neuromorphic
applications through the facilitation of variable resistance states
that mimic synaptic behaviors more closely than traditional
memristive materials, which often have static crystalline structures
that can lead to variability problems or performance limitations at
the nanoscale [11].

Furthermore, fungal memristors may consume less power than
traditional materials due to their unique electrochemical properties.
It has been claimed that some organic materials, including those
derived from fungi, can operate effectively at lower voltages while
maintaining stable switching characteristics--a trait that is crucial
for developing energy-efficient devices for portable electronics and
Internet of Things applications [12]. This can significantly extend
battery life and reduce energy costs in processing and memory
applications, which have become focal points in the research into
neuromorphic systems [13].

Finally, the natural composition and multicellularity of fungal
materials can lead to more naturalistic models for neural networks.
Because these materials are subject to biological processes, they may
inherently incorporate characteristics that resemble biological
neuronal networks, including plasticity and memory capabilities that
could evolve with usage. This biological mimicry could strengthen the
development of more advanced artificial neural networks, enabling
applications such as adaptive learning systems and intelligent sensor
networks [14].

Fungus types

The potential use of common food mushrooms, such as shiitake and
button mushrooms (Agaricus bisporus), as organic memristors is an
emerging area of research that exploits the unique properties of
these fungi [6,7,13]. Memristors, which are non-volatile memory
devices that retain information even without power, can benefit from
the porous structures and electrical properties of the organic
materials derived from mushrooms.

Shiitake mushrooms have been shown to possess a hierarchically porous
carbon structure when activated. This porous structure can enhance
the electrochemical performance of devices, making them suitable
candidates for use in energy storage systems, including
supercapacitors and, potentially, memristors [15]. Highly conductive
carbon materials have been created from shiitake, suggesting that
these materials could be engineered to exhibit memristive behavior [
16]. Shiitake-derived carbon is a sustainable alternative to
traditional materials and can enhance the performance of electronic
devices due to its unique structural properties.

Button mushrooms have also shown significant potential in this
context. Research has indicated that their porosity can be exploited
to create materials with large surface areas, which are essential for
the development of efficient electronic components [17]. The
synthesis of carbon composites from button mushrooms has been
explored, revealing their ability to function effectively in energy
storage applications [17]. Furthermore, the integration of button
mushrooms into electronic systems has been investigated,
demonstrating their potential as substrates for electronic devices [
18].

In addition to their structural properties, the unique biological
characteristics of fungi, including their ability to interact with
various chemical compounds, can be harnessed to develop novel sensing
technologies. For instance, electronic noses have been developed that
use mushroom extracts to detect volatile compounds. These could be
adapted for use in electronic devices that require
environmental-sensing capabilities [19,20]. This intersection of
biology and electronics opens new avenues for creating
multifunctional devices that incorporate the sensory capabilities of
mushrooms.

Radiation, resistance, and resilience

The radiation resistance of shiitake mushrooms has been studied
primarily in terms of their ability to withstand and possibly derive
benefits from exposure to ionizing radiation. This resistance can be
attributed to several biochemical and physiological attributes. A
possible factor is lentinan, a polysaccharide found in the cell walls
of shiitake. Lentinan provides structural integrity and exhibits
immunomodulatory effects that may enhance the mushroom's ability to
respond to environmental stresses, including radiation exposure.
Although some research has suggested that lentinan possesses
properties that may help mitigate oxidative stress [21], there have
been limited studies directly linking lentinan to radiation
resistance in shiitake mushrooms.

Shiitake mushrooms have also shown a notable ability to adapt to
their environmental conditions, including variable radiation levels.
Studies involving fungi in space research have indicated that certain
taxa can enhance their survival through morphological changes or
increased melanin production in response to radiation [22]. This
radiation resistance implies a suitability of fungal electronics for
aerospace applications, where cosmic rays and ambient radiation can
interfere with conventional electronics. Fungi's physical flexibility
and low energy requirements would also be advantageous relative to
conventional solutions [18,19]. These studies have not specifically
addressed shiitake, but the general adaptability observed in fungi
suggests that this species could respond similarly to such
conditions.

Another example of the resilience of shiitake mushrooms is their
ability to maintain their nutritional and bioactive qualities after
irradiation. For example, they retain essential nutrients and
bioactive compounds even after exposure to ultraviolet radiation [23
]. The high content of ergosterol, a precursor to vitamin D, found in
shiitake mushrooms, reinforces their potential for beneficial
outcomes following exposure to radiation because this compound can be
converted into vitamin D[2] when subjected to ultraviolet light [24].

Lastly, shiitake mushrooms could be considered in the development of
dietary supplements or functional foods that could serve a broader
purpose in radioprotection. Their multirole efficacy as a food source
and electrical component emphasizes a sustainable approach to
utilizing biological entities that can withstand environmental
stresses, including radiation. This is especially relevant in
aerospace and exploration contexts, where promoting health in
astronauts could reduce the risks associated with their increased
radiation exposure during missions [22]. Also, shiitake mushrooms can
withstand environmental stresses, including radiation, while
remaining safe for human consumption.

In summary, the radiation resistance of shiitake mushrooms is linked
to the presence of protective compounds, such as lentinan, and their
ability to adapt morphologically. These factors have contributed to
our understanding of their survival strategies and are suggestive of
potential applications in areas where radiation exposure is a
significant concern, such as aerospace and radiation sensing. By
culturing and evaluating the memristive properties of shiitake
mushrooms, we can determine their suitability for use as sustainable,
low-cost bioelectronics.

Methods

Summary

Testing the memristive behavior of shiitake mycelium involved several
steps, the first being culturing the fungi, and then preparing the
samples by drying and rehydrating them. Following this, the most
successfully cultivated samples were electrically characterized using
a test circuit. Additionally, a special circuit was constructed for
further evaluating the feasibility of using mycelium for violate
memory.

Hyphal cultivation

Due to the financial and environmental constraints of this project,
all four evaluated memristors fabricated for our experiments were
composed exclusively of low-cost, organic materials. Based on
previous research, we identified materials such as biocompatible
composites [25,26] as viable candidates for device construction and
programming due to their biodegradability and compatibility with
fungal growth.

The initial phase of experimentation focused on the cultivation of
fungal hyphae in the selected organic growth media. Nine samples were
prepared in standard polycarbonate Petri dishes. The growth
conditions were carefully maintained to promote optimal fungal
development, with a controlled temperature range of 20-22degC, a
relative humidity of 70%, and mixed light exposure to replicate
natural terrestrial conditions. The nutrient substrate consisted of a
mixture of farro seed, wheat germ, and hay, selected for their
organic compositions and ability to support robust fungal growth.
Each sample was inoculated with spores or mycelial plugs of shiitake.

The samples (e.g., see Fig 1) were observed and documented biweekly
to track their growth consistency and morphological development.
Observations including hyphal density, surface coverage, and color
changes were recorded in a structured laboratory logbook. In addition
to these visual inspections, a brief scratch test was performed to
track the progress of the mycelium throughout the substrate. The log
included timestamps and qualitative notes, enabling consistent
comparison across samples and time points.

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Fig 1. Fungal sample with probe points.

Each sample grew a mycelial network that was connected to
conventional electronics.

https://doi.org/10.1371/journal.pone.0328965.g001

Drying and rehydration process

Once full hyphal coverage and structural maturation were achieved
(i.e., when the Petri dish was covered), the samples were
transitioned to the drying phase. The Petri dishes were left in a
well-ventilated area under direct sunlight at room temperature for
approximately seven days to ensure uniform dehydration. The samples
were rotated periodically to avoid uneven hardening. As previously
reported, this process transformed the fungal matrix into a rigid,
disk-like structure while retaining its overall shape and
connectivity [26,27].

Prior to testing, the samples were rehydrated using a fine mist of
aerosolized deionized water. The rehydration was conducted using a
standard commercial spray bottle, held within a distance of 10 cm
from each sample. This brief rehydration step restored the required
level of conductivity without introducing bulk moisture that could
have altered their mechanical integrity.

Electrical characterization

Electrical testing protocols were designed based on theoretical
memristors [6,7]. An alternating current was applied to each sample,
and the corresponding current-voltage (I-V) characteristics were
measured using a digital oscilloscope. As established in previous
works, the test setup used a voltage divider to model multiple
memristors in the same circuit [6,7].

To extract accurate current values, a known shunt resistor was placed
in series with each sample. As shown in Fig 2, voltage readings were
captured across both the sample and the resistor, with Channel 1 of
the oscilloscope measuring the input voltage and Channel 2 capturing
the voltage drop across the shunt resistor. The current values were
then calculated using Kirchhoff's current law, allowing derivation of
the I-V characteristics from the voltage differentials. All waveform
data were exported in comma-separated values (CSV) format for
subsequent digital analysis and visualization.

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Fig 2. Example theoretical test circuit.

The test circuit evaluated the memristive properties of each sample.

https://doi.org/10.1371/journal.pone.0328965.g002

To thoroughly investigate the memristive behavior of the four samples
using mycelium coverage density, voltage sweeps were conducted using
both square and sinusoidal waveforms. The square waves were employed
to detect sharp threshold-based resistance changes, whereas the
sinusoidal inputs provided insights into the more subtle, continuous
mem-fractive behaviors. This dual approach enabled the identification
of hysteresis loops in the I-V curves--a key signature of memristor
functionality.

A square wave was used first, with the peak-to-peak voltage starting
at 200 mV[pp] and increasing. If a sinusoidal wave form exhibited
more promising results, a broader range of frequencies was explored.
The frequencies and voltages used in the initial tests for memristive
properties are detailed in Table 1.

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Table 1. Test parameters for memristive testing of the fungal
samples.

https://doi.org/10.1371/journal.pone.0328965.t001

Accuracy and error were calculated based on how many reads agreed
with the analog threshold, the number of malformed readings, timing
jitter, recording instability, and port delays [28].

[journal](1)

The accuracy was calculated using Equation 1, where accuracy is a
percentage converted from product of correct samples C over the total
number of samples N. The standard error SE was calculated for each
case, as shown in Equation 2.

[journal](2)

A simulated ideal memristive curve was compared against each
experimental result, where the statistical distance d was calculated
between both curves [28]. The distance was used to compute memristive
accuracy [journal] at a particular frequency, as shown in Equation 3.

[journal](3)

Volatile memory testing

In the event that the fungal samples exhibited memristive behavior, a
specialized electronic circuit was designed and implemented to
investigate the volatile memory characteristics of two fungal samples
in series. The test circuit was a voltage divider with memory. The
test involved setting an arbitrary analog voltage value to represent
a high value, and below that threshold was a low value. The frequency
range started at 200 Hz and concluded at 5.85 kHz. Similarly to
previous work, Fig 3 shows the configuration and layout of this
testing circuit [6].

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Fig 3. Theoretical volatile memory circuit.

The samples were evaluated using this model.

https://doi.org/10.1371/journal.pone.0328965.g003

Comparably to previous work in memristive computing, the volatile
memory circuit employed an Arduino UNO microcontroller development
board and a voltage divider consisting of two memristive elements [6,
7]. Given the polarized nature of memristors, the circuit was
designed to allow a voltage of opposite polarity to that used during
read operations to be set. Both voltages used were approximately 5 V.
The Arduino UNO cyclically applied a high signal to a relay
containing a half-rectified sine wave through one of its digital
output pins when reading the memristor bridge, thereby charging the
divider. This process induced an asymmetry in resistance, with the
memristor closest to the input experiencing a reduction in
resistance, while the output-side memristor exhibited an increase.
The voltage across the divider was subsequently read using an analog
input pin, and another digital pin was used to run 5 V across the
divider. The Arduino interpreted the stored state as "on" only when
the measured voltage exceeded a predefined threshold, effectively
enabling volatile memory detection based on the transient resistance
states of the memristors. Ten tests were repeated on each of the four
samples. The physical implementation of this circuit is shown in Fig
4.

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Fig 4. Wired samples.

The volatile memory circuit was implemented using fungal memristors.

https://doi.org/10.1371/journal.pone.0328965.g004

The memristor voltage divider was tested by applying a 5 V[pp]
sinusoidal signal to the memristors for approximately 0.01-0.1 ms.
This signal was delivered via a relay triggered by digital pin 6 of
the Arduino UNO. Following this brief stimulation period, the
sinusoidal input was disabled, and digital pin 5 was activated to
initiate the read phase. Analog voltage measurements were then
acquired through the A1 analog input pin. To minimize the effects of
floating voltages, a 1 MO pull-down resistor was connected to this
pin. Voltage readings were recorded for approximately 0.1-0.10 ms
before the cycle repeated, allowing for rapid and continuous testing
of the memristive behavior.

The measurements were transmitted over a serial communication
interface at a baud rate of 57,600 and were captured as raw text
files for analysis. The data were post-processed and visualized using
a custom Python script based on the matplotlib library, enabling
clear identification of memory retention patterns and resistance
state changes across successive cycles.

Hypothesis

The general testing setup, based on that used in the literature, is
able to indicate memristive behavior in fungal samples. If present,
this behavior would manifest as a characteristic pinched hysteresis
loop in the I-V curves, typically intersecting at or near the
origin--a well-established signature of memristive systems [6,7]. We
hypothesized that such a response would emerge under specific
combinations of voltage amplitude and input frequency. Where
memristive behavior was indicated, volatile memory tests were
conducted.

Results

Overview

The fungal memristors were tested across a range of voltages,
waveforms, and frequencies. Below, we first detail the test inputs
used to explore the memristive properties and generate I-V curves.
Then we present the voltage and frequency (graphical) test results,
followed by the volatile memory test results. Each figure represents
the averaged, smoothed results across the samples.

Voltage testing

The first five tests were conducted to determine which voltage
amplitude produced the most favorable memristive response. These
initial trials revealed that a 1 V[pp] signal yielded the most
consistent and measurable results. As outlined in the Methods
section, the first four of these tests were performed using a square
wave input.

Frequency testing

After identifying 1 V[pp] as the optimal input voltage during the
initial square wave tests (Tests 1-4), the waveform was switched to a
sine wave for further analysis (Tests 5-10). The aim of this phase
was to identify the frequency at which memristive
behavior--specifically a pinched hysteresis loop--became apparent.

In Tests 1-5, the voltage amplitude was optimized using square waves.
Between Tests 5 and 6, the waveform was changed from square to sine.
From Tests 6-10, frequency sweeps were carried out with sine waves to
identify memristive crossing. In Test 11, the voltage range was
expanded at 10 Hz (5 V[pp]) to enhance the response. This revealed
behavior close to that of an ideal memristor. Notably, Test 1 had
already shown consistent linear behavior, indicating resistive
characteristics. The results are shown in Figs 5-13. Fig 14 details a
sample noise profile, and Fig 15 summarizes memristive accuracy.

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Fig 5. Memcapacitive activity.

Plot of a 200 mV[pp] square wave at 200 Hz displaying memcapacitive
behavior.

https://doi.org/10.1371/journal.pone.0328965.g005

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Fig 6. Resistive activity.

Plot of a 20 V[pp] square wave at 200 Hz displaying resistive
behavior.

https://doi.org/10.1371/journal.pone.0328965.g006

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Fig 7. Memcapacitive activity at 200 Hz.

Plot of a 1 V[pp] square wave at 200 Hz displaying memcapacitive
behavior.

https://doi.org/10.1371/journal.pone.0328965.g007

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Fig 8. Further memcapacity activity at 200 Hz.

Plot of a 1 V[pp] sine wave at 200 Hz displaying memcapacitive
behavior.

https://doi.org/10.1371/journal.pone.0328965.g008

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Fig 9. Memcapacitive activity at 100 Hz.

Plot of a 1 V[pp] sine wave at 100 Hz displaying memcapacitive
behavior.

https://doi.org/10.1371/journal.pone.0328965.g009

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Fig 10. Memristive activity at 25 Hz.

Plot of a 1 V[pp] sine wave at 25 Hz displaying memristive behavior.

https://doi.org/10.1371/journal.pone.0328965.g010

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Fig 11. Memristive activity at 50 Hz.

Plot of a 1 V[pp] sine wave at 50 Hz displaying memristive behavior.

https://doi.org/10.1371/journal.pone.0328965.g011

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Fig 12. Increasingly ideal memristive activity at 50 Hz.

Plot of a 1 V[pp] sine wave at 10 Hz displaying memristive behavior.

https://doi.org/10.1371/journal.pone.0328965.g012

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Fig 13. Near ideal memristive behavior.

Plot of a 5 V[pp] sine wave at 10 Hz displaying near-ideal memristive
behavior.

https://doi.org/10.1371/journal.pone.0328965.g013

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Fig 14. Noise profile from measurement from Test 9.

Plot of a noisy 1 V[pp] sine wave at 10 Hz during measurement.

https://doi.org/10.1371/journal.pone.0328965.g014

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Fig 15. Memristive accuracy of initial tests.

Memristive accuracy plotted for Tests 1-11.

https://doi.org/10.1371/journal.pone.0328965.g015

Figs 5-10 show the output of Tests 2-7. The frequency was gradually
reduced until a crossing near the origin was first observed, as shown
in Fig 10. To ensure this result was not an outlier caused by
overshooting the ideal frequency, the test was repeated at a slightly
higher frequency (50 Hz, Test 8, shown in Fig 11). This confirmed
that the optimal response occurred below 25 Hz.

As summarized in Table 2, the frequency was decreased to 10 Hz (Test
9, shown in Fig 12), which produced a clear crossing in the I-V curve
near the -0.4 V region. To enhance the visibility of this behavior,
the voltage was increased to 5 V[pp], which resulted in a more
pronounced memristive signature (Test 10). Fig 13 illustrates this
result, displaying a nearly ideal pinched hysteresis loop indicative
of memristor functionality. The highest accuracy, at 95%, was at a 10
Hz sine wave at 1 V. Fig 14 details the noise from an individual
sample. Fig 15 details the average memristive accuracy of each
configuration.

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Table 2. Summary of frequency, wave, voltage, behavior, and output
measurements.

https://doi.org/10.1371/journal.pone.0328965.t002

Volatile memory experiment

For the volatile memory tests 1 and 2, single read and write
operations were performed across the memristor voltage divider. For
volatile memory test 3, continuous read and write operations were
performed across the memristor voltage divider while the frequency
was gradually increased. The primary results are summarized in Table
3. The results are displayed in Figs 16-20. Averaged nemristive
accuracy is displayed in Fig 21.

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Table 3. Summary of memristive testing and measurements.

https://doi.org/10.1371/journal.pone.0328965.t003

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Fig 16. Volatile memory test 1.

A single write and read over volatile memory.

https://doi.org/10.1371/journal.pone.0328965.g016

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Fig 17. Volatile memory test 2.

Another single write and read over volatile memory.

https://doi.org/10.1371/journal.pone.0328965.g017

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Fig 18. Volatile memory test 3: cyclic memory test at 200 Hz.

Cyclical writing and reading over the fungal volatile memory.

https://doi.org/10.1371/journal.pone.0328965.g018

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Fig 19. Volatile memory test 3: rapid cycle memory test at 700 Hz.

Additional cyclical writing and reading over the fungal volatile
memory.

https://doi.org/10.1371/journal.pone.0328965.g019

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Fig 20. Volatile memory test 3: stressed memory test at 5.85 kHz.

Extreme cyclical writing and reading from volatile memory.

https://doi.org/10.1371/journal.pone.0328965.g020

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Fig 21. Volatile memory test memristive accuracy.

Accuracy for first two tests and cyclic tests.

https://doi.org/10.1371/journal.pone.0328965.g021

Discussion

Overview

Using low-cost materials, shiitake mushrooms were cultured into ideal
memristors. Ideal and non-ideal memristive properties have been
observed previously in fungi, but these required far more complex
interfacing methods [26]. Although several techniques have been
proposed to preserve fungal samples, we obtained experimental
validation that dehydration can preserve the observed properties in a
previously "programmed" sample [27]. Ideal memristor properties are
observed at lower frequencies, but potential latencies can be offset
through massive parallelization, as in nature [26,28,29]. As known
from previous works on fungal memristors, the mycelial structure
contains capacitive, memfractive, and memristive proteins [25,26]. In
the memristive tests, accuracy decreased as the frequency increased.
The observed rapid switching speed of 5,850 Hz, an accuracy of 90% (+-
1%) low energy consumption relative to prior conventional systems,
light weight, and radiation resistance all make fungal memristors
attractive for edge computing, aerospace, and embedded firmware
applications [25-27]. Unlike expensive conventional memristors,
culturing fungal memristors does not require large facilities or rare
minerals. The process can be scaled to grow large systems, which can
be programmed and preserved for long-term use at low cost.

Limitations

Our study was limited by the relatively short timescale of less than
two months. Other researchers have documented memristive properties
in mycelial materials, but their studies also focused on short-term
performance [26]. Another limitation was that only single, relatively
bulky samples were prepared. To truly compete with conventional
devices at the microscale and below, memristors will need to be far
smaller [7,8,11]. Even in the same growth medium, each sample
produced a vastly different culture, and the outcomes have yet to be
fully characterized by electrical properties. However, the
development of these devices is in an early stage, and they could
eventually be miniaturized, especially using improved cultivation
techniques. Complications associated with the growth media were not
explored, although previous research has found that fungi are quite
robust to varying conditions [26].

Future work

Although fungal memristors can be produced at low cost, certain
aspects of the process could be further optimized. First, consistent
cultivation techniques could be improved using three-dimensional (3D)
-printed templates and structures that shape the shiitake mushroom
into the desired geometry. Second, programming could be facilitated
by adding electrical contacts into a 3D-printed cultivation
structure. Finally, long-term use would necessitate preservation,
which could involve a variety of techniques, including dehydration,
desiccation, freeze-drying, certain hydrogels, and special coatings [
27]. By testing devices produced to physical stress conditions, a
combination of these techniques could enable the development of fast,
radiation-resistant, and low-energy memristors grown from low-cost
organic materials. The future of computing could be fungal.

Conclusions

Currently, the fabrication of semiconductor memristors requires
rare-earth minerals and large facilities, and culturing delicate
neural organoids requires a complex chemical environment to be
maintained in a bioreactor. Fungal computing may provide a robust and
accessible alternative. Fungal systems have lower power requirements,
lighter weights, faster switching speeds, and lower industrial
overheads than conventional devices. In this study, fungal memristors
were fabricated, programmed, and tested using shiitake mushrooms and
conventional electronics. Dehydration-based preservation was
successfully explored, demonstrating the robustness of our devices.
When used as RAM, our mushroom memristor was able to operate at up to
5,850 Hz at an accuracy of 90 +- 1%. In addition, shiitake mushrooms
are biodegradable and have demonstrated radiation resistance,
suggesting that the potential applications of fungal computing range
from sustainable computing devices to aerospace technologies.

Acknowledgments

We would like to thank Ryan Lingo and Rajeev Chhajer of the Honda
Research Institute.

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