Myceloom: The Artificial Intelligence of Living Networks

Protocol Specification — A Digital Archaeological Investigation

Josie Jefferson & Felix Velasco
Digital Archaeologists, Unearth Heritage Foundry

with Technical Collaboration from:
Claude 4.5 (Opus & Sonnet) & Gemini (2.5 & 3 Pro)
(Synthetic Intelligence Systems)

Date: January 2026
Version: 1.0
Publication Type: Protocol Specification / Working Paper
Series: The Myceloom Protocol (Part 4 of 8) DOI: https://doi.org/10.5281/zenodo.18344260

Keywords: Myceloom, Artificial Intelligence, Bio-Hybrid Computing, Fungal Intelligence, Collective Intelligence, Unconventional Computing, Reservoir Computing, Distributed Cognition, Mycelial Networks, Symbiotic AI

Abstract

While silicon-based neural networks dominate contemporary AI discourse, the most sophisticated information processing systems on Earth are biological. Mycelial networks—fungal information architectures computing, learning, and adapting for over 400 million years—offer profound lessons for AI development transcending biomimicry. This protocol specification articulates principles for AI systems integrating the distributed cognition and computational properties of living networks, drawing upon advances in unconventional computing, reservoir computing, bio-hybrid robotics, and collective intelligence research. Theoretical and empirical foundations reveal AI development not as creating isolated artificial minds, but as cultivating symbiotic intelligence networks honoring both computational efficiency and organic wisdom. This specification establishes the AI layer of the Myceloom Protocol, defining how artificial and biological intelligence can interface within collaborative network architectures.

I. Introduction: The Convergence of Biological and Artificial Intelligence

In 2025's cascading intelligence revolution, artificial intelligence researchers increasingly study the forest floor. While silicon-based neural networks dominate computational headlines, the most sophisticated information processing systems on Earth—mycelial networks—have been computing, learning, and adapting for over 400 million years.¹: Recent breakthroughs demonstrate that living fungal networks can implement Boolean logic circuits, exhibit memory formation, and demonstrate collective decision-making paralleling artificial neural architectures.²: The convergence of biological and artificial intelligence represents not merely an engineering opportunity but a fundamental reconceptualization of intelligence itself.

The field lacks precise terminology to describe this intersection. Academic papers describe "bio-hybrid computing," "fungal electronics," and "nature-inspired AI," but these multi-word phrases gesture toward something demanding a single, resonant term.³: Absent adequate vocabulary constrains both theoretical development and practical implementation.

Through digital archaeological excavation, the research foundry unearth.im has identified "myceloom" as the framework for AI systems learning from and interfacing with mycelial intelligence.⁴: Like the symbiotic networks it describes, this term weaves together artificial and biological cognition into a unified approach to distributed intelligence. Theoretical foundations and practical implications of myceloom thinking for artificial intelligence development draw upon multiple scholarly traditions to articulate principles for designing AI systems enhancing rather than replacing the natural intelligence of living systems.

II. The Architecture of Biological Computing

A. Fungal Networks as Computational Substrates

Recent research has shattered the boundary between biological and artificial computation. Andrew Adamatzky and colleagues at the University of the West of England demonstrated that living mycelium networks can implement a "wide range of Boolean circuits" through the non-linear transformation of electrical signals.⁵: These fungal composites exhibit "rich dynamics of neuron-like spiking behaviour" and demonstrate genuine computation embedded within living materials.⁶: The work, published in peer-reviewed journals including Scientific Reports and Interface Focus, establishes that fungal networks are not merely metaphorically computational but literally implement logical operations.

The mechanisms underlying fungal computation involve calcium waves and associated electrical potential changes propagating through mycelial networks.⁷: Adamatzky's team showed that these excitation waves can implement computation by encoding Boolean values as spikes of extracellular potential. The researchers "derive sets of two-inputs-on-output logical gates implementable the fungal colony and analyse distributions of the gates."⁸: The distribution of logical functions depends on environmental and physiological conditions of the mycelium-bound composites, providing what the researchers describe as "computational characterisation of the fungal material states."

Implications extend far beyond biological curiosity. Fungal networks process information through millions of low-power connections, achieving complex computation without the massive energy requirements of contemporary AI systems.⁹: While artificial neural networks consume gigawatts of power in centralized data centers, mycelium achieves distributed intelligence through inherently efficient parallel processing.¹⁰: This efficiency differential has captured AI researchers' attention as they seek alternatives to energy-intensive machine learning architectures, particularly amid mounting concerns regarding large-scale AI environmental sustainability.

B. Mycelial Network Topology and Information Processing

Mycelial network topology reveals sophisticated organizational principles paralleling findings in network science. Research on fungal network architecture demonstrates that mycelium develops "scale-free" network properties; the same topological features found in neural networks, social networks, and the internet.¹¹: Mark Fricker, Lynne Boddy, and colleagues documented that fungal networks exhibit "efficient foraging strategies" and "adaptive resource allocation" emerging from their distributed structure.¹²

The largest known fungal organism, Armillaria bulbosa, spans over 15 hectares and contains an estimated trillion elementary processing units connected through mycelial networks.¹³: This biological computer operates through distributed decision-making, with each hyphal junction acting as a processing node in a vast network architecture predating digital technology by geological ages. Studies reveal that individual hyphal tips respond to local conditions while contributing to network-wide patterns of resource allocation, threat response, and growth coordination.¹⁴

These networks demonstrate "network intelligence": cognitive capabilities arising from the structure and dynamics of connections rather than individual processing power.¹⁵: Network behavior cannot be predicted from individual hyphae behavior; intelligence emerges from their interactions. This emergent property suggests AI development might focus less on building more powerful individual models and more on creating sophisticated networks of interconnected systems demonstrating collective intelligence.

III. Unconventional Computing: Beyond Silicon

A. The Field of Unconventional Computing

The academic field of unconventional computing provides theoretical grounding for myceloom approaches. Unconventional computing investigates computational processes occurring outside traditional silicon-based architectures, including computation in biological, chemical, and physical systems.¹⁶: This field recognizes that computation is not the exclusive province of digital computers but rather a fundamental property of many dynamical systems.

Research in evolution-in-materio demonstrates that materials contain rich properties exploitable to solve computational problems.¹⁷: Rather than abstracting computation away from physical substrates, this approach exploits intrinsic material dynamics for information processing. Studies show that diverse materials—from liquid crystals to nanowire networks to biological tissues—can perform computations when appropriately configured.¹⁸

The connection to myceloom thinking is direct: if computation can occur in diverse physical substrates, then biological networks represent not merely inspiration for AI design but potential computational platforms themselves. This recognition motivates research into hybrid systems combining biological and artificial components, exploiting each component's unique capabilities.

B. Reservoir Computing: Harnessing Dynamical Systems

Reservoir computing provides a particularly relevant computational framework for understanding myceloom architectures. Reservoir computing (RC) originated in the early 2000s as an approach utilizing dynamical systems as reservoirs—nonlinear generalizations of standard bases—to adaptively learn spatiotemporal features and hidden patterns in complex time series.¹⁹: Unlike conventional neural networks requiring extensive training of all parameters, RC systems train only a simple readout layer while leaving reservoir dynamics fixed.

A comprehensive Nature Communications review identifies reservoir computing as a promising direction for lightweight, energy-efficient AI systems.²⁰: The authors note that "biological systems such as human brains are able to accomplish highly accurate and reliable information processing across different scenarios while costing only a tiny fraction of the energy that would have been needed using big neural networks." Reservoir computing offers a computational paradigm more aligned with biological efficiency.

The connection to mycelial networks is profound. Researchers demonstrated that mycelium can function as a physical reservoir computer, processing information through its natural growth and adaptation patterns.²¹: These "mycelium chips" represent hybrid architectures where the biological substrate itself performs information processing otherwise requiring extensive artificial computation. Unlike traditional artificial neural networks requiring extensive training algorithms, mycelial systems demonstrate inherent learning capabilities emerging from their biological architecture.

Research on morphologically tunable mycelium chips demonstrates that fungal networks can serve as physical reservoir computers.²²: The mycelium's electrical activity—its pattern of voltage spikes and signal propagation—performs computational transformations on input signals. By reading out these transformed signals and training a simple linear classifier, researchers accomplish pattern recognition and other computational tasks. The biological substrate does the heavy computational lifting; the artificial component merely interprets the results.

IV. Bio-Hybrid Systems: Integration of Living and Artificial

A. Bio-Hybrid Robotics

Bio-hybrid robotics demonstrates practical applications of integrating biological and artificial systems. A comprehensive npj Robotics review documents how "new robotics solutions have been developed that harness the adaptability of living muscles, the sensitivity of living sensory cells, and even the computational abilities of living neurons."²³: These systems represent genuine integration rather than mere imitation; biological components perform functions artificial systems cannot match.

Bio-hybrid robots integrate living organisms—including cells, tissues, and whole organisms—with synthetic materials to create systems with capabilities exceeding either component alone.²⁴: Living materials offer intrinsic softness, environmental safety and compatibility, and more efficient energy conversion than traditional robots. Research at ETH Zurich and other institutions demonstrates bio-hybrid robots capable of various motion abilities including swimming, bending, rotating, and crawling.²⁵

The bio-hybrid approach aligns with myceloom principles by demonstrating that artificial intelligence need not replace biological intelligence but can be enhanced by it. Rather than abstracting biological principles into purely artificial implementations, bio-hybrid systems maintain living components providing capabilities—adaptation, self-repair, environmental responsiveness—remaining difficult to achieve artificially.

B. Fungal Integration in Artificial Systems

Cornell University researchers developed biohybrid robots integrating living mycelium into electronic systems, creating machines that "sense and respond to the environment" through biological computation.²⁶: These systems represent genuine myceloom architecture: artificial intelligence enhanced by biological network integration rather than biological simulation. The living mycelium provides environmental sensing capabilities informing the robot's behavior.

The integration exploits mycelium's natural responsiveness to environmental stimuli. Fungal networks respond to light, temperature, chemical gradients, and mechanical pressure through changes in their electrical activity and growth patterns.²⁷: By interfacing artificial systems with these biological sensors, researchers create hybrid architectures where the biological component provides information guiding artificial decision-making.

Research demonstrates that machines connected to living mycelial networks exhibit enhanced environmental responsiveness compared to purely digital systems.²⁸: The biological component provides not merely sensing but pre-processing; the mycelium's response to environmental conditions already encodes significant information the artificial system can exploit. This represents a division of labor where biological and artificial components each perform functions they are well-suited for.

V. Distributed Cognition and Collective Intelligence

A. Theoretical Foundations of Distributed Cognition

The theory of distributed cognition provides conceptual grounding for understanding myceloom approaches to AI. Distributed cognition, developed by Edwin Hutchins and colleagues, proposes that cognitive processes are not confined to individual minds but distributed across persons, artifacts, and environments.²⁹: Cognition emerges from interactions among components rather than occurring within isolated processors.

Research on collective cognition demonstrates that network topology shapes collective behavior and intelligence.³⁰: A Philosophical Transactions of the Royal Society B review documents how "social network topology shapes collective cognition": the structure of connections between individuals affects what the collective can accomplish.³¹: This finding has direct implications for AI development; AI system architecture affects collective capabilities.

The distributed cognition framework suggests intelligence need not be concentrated in individual processors but can emerge from appropriately structured networks. This aligns with observations of mycelial networks, where intelligence emerges from distributed interactions rather than centralized control. The implication for AI is that developing more intelligent systems may require focusing on network architecture as much as individual component capabilities.

B. AI-Enhanced Collective Intelligence

Recent research examines how AI can participate in collective intelligence systems rather than operating in isolation. A Patterns review conceptualizes "a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers."³²: The authors propose that "humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation."

This framework aligns with myceloom principles by positioning AI not as replacement for human or biological intelligence but as participant in hybrid cognitive systems. The goal is not artificial general intelligence superseding human cognition but rather AI systems integrating productively with other forms of intelligence: human, biological, and artificial.

Research on emergent collective memory in decentralized multi-agent AI systems demonstrates how collective capabilities can emerge from distributed interactions.³³: Studies show that multi-agent systems can develop collective memory through interplay between individual agent memory and environmental trace communication, creating "spatially distributed collective memory without centralized control." This emergence of collective properties from distributed components mirrors the emergence of network intelligence in mycelial systems.

VI. Neuromorphic Computing: Biological Inspiration for Hardware

A. The Neuromorphic Paradigm

Neuromorphic computing provides another framework for understanding myceloom approaches. Neuromorphic computing focuses on developing novel computing systems operating at a fraction of the energy of current transistor-based computers, often deviating from von-Neumann architecture and drawing inspiration from biological principles.³⁴: The field seeks computational architectures processing information more like biological systems.

Research demonstrates that neuromorphic approaches offer significant advantages for certain computational tasks. A Springer review notes that reservoir computing on neuromorphic hardware has shown promise due to "computational efficiency and the fact that training amounts to a simple linear regression."³⁵: Both spiking and non-spiking implementations have been developed, demonstrating biological-inspired approach versatility.

The connection to myceloom thinking involves recognizing that biological systems have evolved computational strategies potentially more efficient than current artificial approaches for many tasks. Rather than forcing all computation through silicon architectures optimized for different purposes, neuromorphic approaches seek computational substrates better suited to the tasks at hand, including potentially biological substrates themselves.

B. Biocompatible Computing Platforms

Research on reservoir computing with biocompatible organic electrochemical networks demonstrates practical implementation of biologically-inspired computing.³⁶: A Science Advances study reports organic networks demonstrating "complex nonlinear dynamics" and "features typical of biological cortical systems (e.g., recurrency, short-term memory, and E/I balance)."³⁷: Researchers achieved 88% accuracy in classifying arrhythmic heartbeats using these biological-compatible computational platforms.

These systems suggest pathways toward computing platforms interfacing directly with biological systems. The authors envision "lightweight, noninvasive implants, capable of monitoring biosignals, and perform 'online' computation without the aid of energy-consuming software."³⁸: Such systems would represent myceloom architectures in the most literal sense: computation occurring through biocompatible substrates integrating with living systems.

Development of sustainable memristors from shiitake mycelium demonstrates that fungal materials can provide "scalable, eco-friendly platforms for neuromorphic tasks."³⁹: These biological processors offer alternatives to resource-intensive silicon architectures while demonstrating computational capabilities paralleling artificial neural networks. The material basis of computation shifts from extracted and processed minerals to cultivated biological materials.

VII. The Myceloom Framework: Principles for Symbiotic Intelligence

A. Core Principles

Drawing from surveyed research traditions, the myceloom framework articulates principles for AI development integrating biological and artificial intelligence:

Distributed Processing: Following mycelial network example, myceloom architectures emphasize distributed rather than centralized computation. Intelligence emerges from interactions among network components rather than residing in individual powerful processors. This approach offers resilience (the system continues functioning despite individual component failures) and efficiency (computation occurs locally where information is available).

Biological Integration: Rather than merely imitating biological systems in artificial substrates, myceloom approaches maintain living components performing functions they are uniquely suited for. This integration exploits capabilities—adaptation, self-repair, environmental responsiveness, energy efficiency—remaining difficult to achieve artificially.

Emergent Intelligence: Myceloom systems recognize that collective intelligence can exceed individual component capabilities. The goal is not maximizing individual processor power but optimizing network architecture for emergent collective capabilities. This principle aligns with both mycelial network organization and distributed cognition theory.

Symbiotic Enhancement: Following the collaborative rather than competitive model of mycorrhizal networks, myceloom AI systems aim to enhance rather than replace other forms of intelligence. The goal is productive integration with human cognition, biological systems, and environmental contexts rather than autonomous operation independent of other intelligence.

B. Computational Symbiosis: The Living Machine Interface

Myceloom systems suggest a new paradigm for artificial intelligence, one recognizing intelligence as inherently collaborative rather than competitive. Recent advances in fungal computing reveal that living mycelium can implement reservoir computing, where the biological substrate itself performs information processing.⁴⁰: These hybrid architectures represent a fundamental shift from AI as artificial replacement for biological intelligence to AI as enhancement of biological capabilities.

This symbiotic approach offers solutions to some of AI's most pressing challenges. While contemporary machine learning systems require massive datasets and energy-intensive training processes, myceloom architectures exploit biological intelligence "trained" by hundreds of millions of years of evolutionary optimization.⁴¹: Research shows that AI systems inspired by mycelial principles demonstrate superior resilience, energy efficiency, and adaptive capacity compared to traditional centralized AI systems.⁴²

The symbiosis is bidirectional. Just as AI systems benefit from biological integration, artificial augmentation can enhance biological systems. Bio-hybrid robots demonstrate enhanced capabilities through combining biological and artificial components.⁴³: The integration produces systems with capabilities exceeding either component alone: genuine symbiosis rather than parasitism or competition.

VIII. Applications and Implications

A. Practical Implementations

Current research demonstrates practical applications for myceloom architectures across multiple domains. Fungal computing researchers show that mycelial networks can solve complex optimization problems, including shortest path calculations, network topology optimization, and adaptive resource allocation.⁴⁴: These biological computers operate through environmental programming; modifying growth conditions to reprogram network geometry and computational behavior.

Bio-hybrid robotics research demonstrates applications in environmental monitoring, drug delivery, and search-and-rescue operations.⁴⁵: Bio-hybrid systems offer advantages in environments where conventional robots struggle: wet, constrained, or biologically sensitive contexts. Living components provide capabilities including self-repair, environmental responsiveness, and efficient energy utilization remaining difficult to achieve artificially.

Machine learning researchers implement myceloom principles through decentralized AI architectures distributing computational tasks across networks of simple processors, mimicking how fungi allocate resources based on environmental needs.⁴⁶: These systems demonstrate myceloom architecture democratizing potential by reducing infrastructural requirements while maintaining sophisticated computational capabilities.

B. Sustainability and Energy Efficiency

Biological computing energy efficiency offers significant sustainability advantages. Contemporary large language models and other AI systems consume substantial energy during both training and inference.⁴⁷: AI development environmental impact has become a significant concern as systems scale. Myceloom approaches offer an alternative development trajectory with reduced environmental footprint.

Biological computing systems operate at far lower energy levels than silicon alternatives. Mycelial networks process information through chemical and electrical signaling requiring minimal energy input compared to data center power demands.⁴⁸: While biological systems cannot match silicon's raw computational speed, they achieve remarkable efficiency for their computations. For applications where efficiency matters more than speed—sensing, adaptation, pattern recognition—biological approaches may prove superior.

Cultivating computational materials rather than extracting them represents a paradigm shift in how computing infrastructure relates to environmental systems. Rather than mining minerals and manufacturing processors, myceloom approaches suggest growing computational substrates from renewable biological materials.⁴⁹: This shift aligns computing development with broader sustainability goals.

IX. Challenges and Future Directions

A. Technical Challenges

Significant technical challenges remain in developing myceloom architectures. The timescale problem—matching computational timescales between biological and artificial systems—presents ongoing difficulties.⁵⁰: Biological processes often operate at different temporal scales than electronic computation, requiring careful interface design.

Maintaining living systems within artificial frameworks requires addressing biological needs including nutrition, environmental conditions, and waste removal.⁵¹: Unlike silicon components remaining stable indefinitely, biological components have lifespans and require ongoing maintenance. Developing sturdy bio-hybrid systems requires solving problems of biological sustainability that traditional engineering approaches do not address.

Scalability presents another challenge. While laboratory demonstrations prove biological computation principles, scaling to practical applications requires addressing biological variability, environmental sensitivity, and integration complexity.⁵²: The transition from proof-of-concept to deployable systems represents a significant engineering challenge.

B. Ethical Considerations

Integrating living systems into computing architectures raises ethical questions the field is only beginning to address. Research on bio-hybrid robotics ethics documents emerging considerations including the moral status of living components, the appropriateness of various applications, and environmental implications of creating novel bio-artificial hybrids.⁵³

Questions about biological computing component moral status depend partly on what organisms are involved. Using fungal networks raises different considerations than using animal tissues or neural cells. The field requires frameworks for ethical evaluation accounting for the diverse biological components potentially involved in myceloom systems.⁵⁴

Environmental implications extend beyond sustainability benefits to include potential risks. Releasing bio-hybrid systems into environments could have unintended ecological consequences. Responsible development requires careful consideration of containment, environmental impact assessment, and end-of-life management for biological computing components.⁵⁵

X. Conclusion: The Philosophy of Symbiotic Intelligence

The linguistic innovation of "myceloom" provides essential terminology for navigating the convergence of biological and artificial intelligence. Rather than describing "bio-hybrid AI systems with fungal network integration," one speaks of myceloom architectures and immediately conveys essential qualities: biological, collaborative, adaptive, intelligent. This precision enables clearer thinking about AI development honoring both computational efficiency and organic wisdom.

The convergence of fungal computing research, reservoir computing theory, bio-hybrid robotics, and collective intelligence scholarship points toward unified understanding of intelligence as inherently distributed and potentially symbiotic. Individual processors—whether biological or artificial—achieve fullest expression through integration into collaborative networks. Networks achieve highest capabilities through appropriate composition and connection of diverse components. The health and capability of each depends upon and enables the health and capability of all.

As we advance toward more sophisticated artificial intelligence, mycelial networks beneath forest floors offer profound lessons about distributed cognition, adaptive learning, and symbiotic collaboration. The future of AI may lie not in perfecting isolated artificial minds, but in weaving them into living networks connecting all intelligent life. The myceloom framework captures this evolution: artificial intelligence systems growing like fungi, adapting like living networks, demonstrating the collaborative intelligence necessary for addressing complex global challenges.

In this convergence of ancient biological wisdom and cutting-edge technology lies not just computational efficiency, but a pathway toward AI enhancing rather than replacing the natural intelligence of living systems. AI developed according to myceloom principles can achieve what neither isolated artificial systems nor unaugmented biological systems can accomplish: sustainable intelligence honoring both computational power and organic wisdom. This is not utopian aspiration but empirically grounded possibility, documented in laboratory demonstrations, theoretical frameworks, and emerging practical applications. The substrate for artificial intelligence lies not in competition with biological intelligence but in connection with it, not in replacement but in enhancement, not in isolation but in integration.

Notes

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Digital Archaeological Investigation conducted by unearth.im Research Foundry. This work is intended for publication at myceloom.ai.