The future of advanced computing may not come from a clean room or a silicon wafer, it could grow from a compost heap and a shiitake mushroom. This new frontier, called fungal computing (or mushroom computing), merges engineering and biology to create living computers made from mycelial networks. Researchers believe these biocomputers could power the next generation of neuromorphic, self-healing, and biodegradable processors.
This blending of biology and technology challenges the limits of classical, binary machines and promises a path toward eco-friendly, adaptive electronics. Two main scientific methods drive this revolution: one focuses on sustainable memory storage, and the other builds adaptive processors from living fungal tissue.
What is Fungal computing?
Fungal computing, also known as mushroom computing or biocomputing, is an emerging field of neuromorphic engineering where scientists build computers from fungal mycelium, the underground network of threads connecting mushrooms. These living systems can store, process, and transmit information using electrical impulses, creating biodegradable, self-healing processors that could replace silicon chips in sustainable technology.
The field was pioneered by Dr. Andrew Adamatzky at the Unconventional Computing Laboratory (UWE Bristol), where researchers study how mycelial circuits can perform computation similar to neurons in the human brain.
The core technical split: Memory versus processor
Scientists currently work on two distinct paths in the lab. These paths mirror the core components of every computer system: memory storage and the central processing unit, or processing. Researchers build the synapses and the neurons of a biological computer using different fungal species and techniques.
We compare the focus and function of the two leading research hubs below:
| Feature | OSU Shiitake Memristors | UWE Bristol Fungal Wetware |
| Primary Goal | Create a sustainable, non-volatile memory component. | Create an adaptive, massively parallel processor/logic unit. |
| Biological Analogy | The Synapse (A memory storage point). | The Neuron and Neural Network (The computational core). |
| State of Matter | Primarily tested as dehydrated/preserved material for stability. | Living “Wetware” using dynamic, growing networks. |
| Key Output | Memristors that retain an electrical state without power. | Complex logic, pattern recognition, and problem-solving capability. |
The foundational divide: Neuromorphic versus Von Neumann
We must first understand the fundamental architectural difference between the mushroom processor and every modern chip (like x86 or ARM). Fungal processors belong to neuromorphic computing. This system copies the brain. Traditional chips run on the Von Neumann architecture, which is almost 100 years old. This difference dictates all capabilities and limitations.
Fungal processors, by contrast, operate using neuromorphic computing, where memory and processing occur together, similar to how the brain works.
| Feature | Fungal/Neuromorphic Processor | Traditional x86/ARM Processor |
| Architecture | Non-Von Neumann | Von Neumann |
| Data Flow | Memory and processing are co-located (In-Memory Computing). | Memory (RAM) and the Processor (CPU) are separate units. |
| Computational Method | Analog/Spiking Activity (Continuous signals). | Digital/Binary (Discrete 1s and 0s). |
| Processing Style | Massively Parallel & Asynchronous (Event-driven). | Sequential & Synchronous (Clock-driven). |
| Learning | On-Device Learning (Hardware physically adapts). | Software-Defined (Hardware is fixed). |
| Material | Organic/Biodegradable | Inorganic/Silicon (Uses rare-earth minerals). |
| Limiting Factor | Translation Loss (Interfacing with binary systems). | Von Neumann Bottleneck (Moving data wastes time and power). |
The Von Neumann Bottleneck is the key problem. In x86 or ARM systems, energy and time are constantly wasted because the system moves data between the separate CPU and memory units. Fungal processors avoid this by combining memory and computation, which allows excellent energy savings and quick adaptation.
The Sustainable Component: The Memristor Breakthrough
At Ohio State University (OSU), researchers discovered that shiitake mushrooms can function as bio-memristors, biological memory devices whose resistance changes based on prior electrical activity.
These mushroom-based components remember their last state, making them potential replacements for traditional silicon memory chips.
We must first explain the core concept:
- Memristor: This word mixes “memory” and “resistor.” A memristor is an electronic component that can also remember the amount of electrical charge that flowed through it before, whereas regular resistors keep a fixed resistance. A memristor’s resistance changes based on its history.
- The Shiitake’s Role: The mushroom material shows this important memory effect. Researchers apply a voltage that makes the fungal material change its electrical resistance stably and predictably, while retaining information about its previous electrical state.
The OSU Methodology: Training the Fungal Chip
Engineers did not simply crop a mushroom and attach a wire. They implemented a controlled sequence of biological and electrical processes. These steps stabilized the fungi and trained their conductive pathways:
- Cultivation and Preservation: Researchers first grew samples of Shiitake and button mushrooms. They dehydrated the specimens after they matured. This preservation step ensures long-term viability and stability in a final electronic device.
- Circuit Integration: Scientists attached the preserved mushroom tissue to custom electronic circuits. They connected electrical wires and probes at various points. They knew different parts of the fungi have unique electrical properties.
- Electrical Training: The core step exposed the mushroom device to controlled electric currents at various voltages and frequencies. This training lasted over two months. This method “trained” the fungi. It caused its internal conductive pathways to stabilize. The pathways then reproduced the necessary memory effects seen in semiconductor chips.
These biomemristors, biological devices that store memory through electrical resistance, demonstrated switching speeds of up to 5,850 times per second. They achieved about 90% accuracy, use little power while on standby, and are also completely biodegradable. This solves the huge electronic waste problem caused by silicon.
The adaptive core: Mycelial “Wetware” processors
While the OSU team builds the memory, the Unconventional Computing Laboratory at the University of the West of England (UWE Bristol) focuses on the processor. They use the entire fungal network as a living computational engine. This is how the Mycelial Motherboard takes shape.
This method uses the deep complexity of the fungal nervous system. It tackles complex computational tasks that challenge classical binary computers:
- “Wetware” Defined: This word refers to putting living biological matter into an electronic system. This is different from hardware or software. The fungus itself becomes a functional, integrated computer component.
- The Mycelial Network: The visible mushroom (fruiting body) is only a small part of the organism. The mycelium forms a massive, branching web of connections underground. This is like a plant’s root system. This network works as a three-dimensional motherboard, and the scientists interface with it using electrodes.
The Key Insights:
- The mycelium network acts like a 3D circuit board, transmitting analog signals across its web.
- Researchers attach electrodes to interface with the fungus and “zap” signals into the network.
- The fungus responds and reconfigures its pathways, physically learning through experience.
- By adjusting nutrients and temperature, scientists can reprogram the mycelial geometry.
This is the foundation of organic AI, a biologically driven system that evolves rather than executes code.
The Bristol Methodology explained: Building a living motherboard
The Bristol approach creates a constantly active system that can change its own hardware. The steps focus on keeping the network alive and also focus on establishing a data feedback loop:
- Fungal Selection: Researchers selected stable, proper fungi. They focused on the Basidiomycota class. This includes Oyster, Ghost, and Cordyceps species. They chose these fungi for their highly developed, consistent mycelial networks.
- Motherboard Integration: Scientists produce actual motherboards with the mycelium growing out of them. This process embeds the living organism directly into a custom electronic base.
- I/O Loop Establishment: The crucial step creates a “completed loop.” Researchers send external electrical signals to “zap” the network. They receive corresponding electrical pulses back via electrodes in the mycelium. The signal presence or absence translates into one-half of a binary data language.
- Reprogramming the Network: The team showed that they can reprogram the geometry and graph-theoretical structure of the mycelium. They change environmental conditions to do this. They adjust things like nutrient availability and temperature. This means the computer physically changes its own processing pathways to find the best task solution.
The fungal network does calculations in parallel. It handles multiple tasks simultaneously, which is better than sequential binary systems, and also evolves and learns. They physically change their pathways to solve new problems. This shows a form of inherent, organic artificial intelligence.
The Fungal future: Applications and the translation challenge
Together, the OSU and Bristol research efforts outline the difference between machine logic and living logic. Fungal computers can learn, heal, and grow, making them ideal for biodegradable electronics, self-repairing devices, and environmental sensors where maintenance is impossible.
Market Applications and Disruptive Potential
- Zero-Waste Electronics: Fungal components drastically reduce e-waste. They also reduce the need for rare-earth minerals. This makes them perfect for high-volume, disposable electronics. Examples are temporary sensors or smart packaging.
- Hyper-Resilience: Fungal computers can self-repair. They can survive environmental challenges. These include water exposure and radiation. They become top candidates for remote infrastructure, deep-sea sensors, or aerospace missions. Maintenance is impossible in these places.
- Advanced Wearable and Medical Tech: Living fungal apparatuses sense and process information from the environment. They process the body’s chemical processes. Current electronic wearables cannot do this. Fungal tech allows for ultra-advanced, real-time medical monitoring.
- Sustainable AI: Systems that merge biological and digital intelligence.
The Crucial Technical Hurdle: Translation Loss
Despite these breakthroughs, one major obstacle remains: translation loss.
Fungi communicate using a complex analog language of electrical pulses, with far more variation than binary code allows. When converted into 1s and 0s, much of the information is lost, similar to explaining a complex idea with only a few words.
To overcome this, engineers are developing bio-analog-to-digital converters that can capture the full richness of fungal communication. Solving this challenge will allow fungal computers to integrate with traditional AI and IoT systems.
The Fungal future: Toward living computers
Fungal computing represents a new paradigm where hardware grows, adapts, and heals itself. As these systems evolve, they could bridge biology, AI, and sustainable materials science, leading to eco-friendly, adaptive computers that transform how we build and power technology.
The next generation of computers might not be manufactured — they might grow.
