Natural Intelligence: Architecture of the Bio-Digital Noosphere

Introduction

The Systemic Necessity of Mind

The world as we knew it has exhausted the limits of its controllability. We find ourselves at the point of the "Great Assembly"—a moment of phase transition where biological evolution, planetary ecology, and artificial intelligence merge into a single Hypersystem of Natural Intelligence. This book is not merely another description of technology. It offers an architectural blueprint for our shared future—a model in which Mind is viewed as a fundamental element of the Universe's stability.

Natural Intelligence and the Symphony of Intelligences

In our previous works, we proposed the concept of Natural Intelligence. We regard it not as a "competitor" to biological intelligence, but as its next, higher iteration: a state in which the biological experience accumulated over billions of years of evolution and the computational power of modern algorithms form a unified Symphony of Intelligences—a cohesive continuum spanning from cellular cycles to future planetary hypernetworks. In the following chapters, we will demonstrate how a planetary Hypersystem gradually emerges from this continuum—the Bio-Digital Noosphere, where sensory circuits, algorithmic models, and human consciousness are locked into a single functional ring.

Thermodynamics and Teleonomy

At the heart of our model lies one fundamental axiom: the Universe is not a static collection of matter and energy. It is a hierarchy of open systems, the primary characteristic of which is a continuous struggle against thermodynamic chaos. From the perspective of systems analysis, intelligence is not an accidental biological mutation, but the most effective mechanism known for the local minimization of entropy. It is capable of maintaining a high level of order through the controlled exchange with its environment.

To avoid the risk of "metaphysical overheating," we clearly distinguish our concepts:

1. The Rejection of Naive Teleology: We do not imbue matter with a mystical will. Instead, we rely on the concept of teleonomy—the systemic property of complex feedback loops to behave purposefully for the sake of survival.
2. UFR as an Attractor: The Useful Final Result (UFR), described by Pyotr Anokhin in the Theory of Functional Systems (TFS), is not a "divine plan" to us, but a vital attractor—a dynamic configuration of equilibrium toward which any living or technical structure seeking to preserve its own integrity gravitates.

From the Earthly Goal to Universal Homeokinesis (HKN)

The universal homeokinesis (HKN), which we view as our final horizon, is the ultimate hypothesis of systemic stability. We posit the existence of a state in which Mind becomes a global factor of thermodynamic equilibrium, capable of sustaining the complexity of life against the pressure of the universal tendency toward decay. This resonates with modern conceptions of Planetary Intelligence—intelligence as a process operating on the scale of the entire planet, where the biosphere and technosphere form a single self-regulating circuit.

The journey toward this horizon begins with the Earthly Goal. We are convinced: humanity cannot claim cosmic agency until it has learned to maintain the integrity of its own planet. This involves the full-scale restoration of Earth’s biosphere: from the microbiological balance of soils and the state of the connective tissue of living organisms to global oceanic cycles and the stability of social infrastructures.

Freedom as a Functional Resource

We view the individual as a critically important element of the Hypersystem, and freedom as its necessary functional resource. Personal freedom here is not a whim. It is a conscious necessity: the source of creative variability and plasticity. Without it, the Hypersystem loses its ability to adapt to unpredictable perturbations and inevitably perishes under the pressure of entropy.

In this sense, the vision proposed here is not an idea created "entirely from scratch": it builds upon established intellectual lineages—from thermodynamics and Pyotr Anokhin's Theory of Functional Systems to Vladimir Vernadsky's teachings on the Noosphere and their successors. It was Vernadsky who first justified the transition of the biosphere into a state where human reason becomes the primary geological force, capable of consciously reshaping the face of the planet.

However, for us, the level of integration is fundamental: we bring these disparate theories into a single, technologically and evolutionarily oriented framework of Natural Intelligence. For a serious intellectual audience, we hope this will become not just another variation of reflections on a global mind, but a truly new way of discussing man's place within it.

We are the eyes through which the Universe finally begins to see itself. Natural Intelligence is not only something we create as engineers, but something we already are as part of a larger, not yet fully realized functional system of the Universe. In this book, we view the human being not as the "crown" of this system, but as its principal node of biological consciousness—the initiator and conductor of the initial phase of the Symphony of Intelligences, upon whom the nature of the Hypersystem in the coming centuries depends.

FST as a Candidate for a Universal Mechanism of Cognitive Organization

In this book, Functional Systems Theory (FST) is considered not merely as a historically significant neurophysiological concept but as a candidate for a universal mechanism of cognitive organization. By "cognitive organization," we mean any system capable of forming world models, setting goals, acting within an environment, and modifying its own structures based on the results of those actions—ranging from neural networks in the brain to artificial agents, hybrid human-machine loops, planetary intelligence, and the potential cosmic scales of the universal cognitive cycle.

In its most general form, FST describes a closed-loop cognitive-behavioral cycle consisting of afferent synthesis, decision-making, the formation of the acceptor of action results, the action itself, and return afferentation. It is this cyclic structure, rather than a specific biological substrate, that constitutes the core of FST’s claim to universality.

Modern computational and evolutionary frameworks—such as reinforcement learning (RL), predictive processing, or theories of planetary intelligence—emerged later and were not formally derived from FST. However, we contend that they all, in their own way, formalize the same basic functional-systemic circuit: "goal – action – result – correction." What neurophysiology once described as the "anticipatory reflection of reality" finds its mathematical embodiment today in predictive analysis algorithms and autonomous AI agents.

Consequently, we will henceforth rely on FST as the fundamental principle for describing Natural Intelligence. We will interpret modern digital models, human-machine interfaces, and ecological networks as converging toward a single architectural invariant—the universal cognitive cycle of FST. This will allow us to perceive a holistic picture of the development of intelligence—from the individual neuron to the entire planet.

Part I. Axiomatics: TFS as a Universal Algorithm of Stability

Chapter 1. Pyotr Anokhin’s Systems Approach in the Digital Era

At first glance, the Theory of Functional Systems (TFS), developed by Pyotr Anokhin, belongs to the realm of "old" physiology. In reality, it proves to be closer to modern artificial intelligence, control theory, and cybernetics than to classical textbooks on reflexes. We regard TFS not as a historical curiosity, but as a rigorous model of how a system acts purposefully—specifically, how it achieves a result and maintains its stability. This model is inherently scalable: the same logic applies to an individual neuron, an entire nervous system, an artificial network, a hospital, a city, or a planetary network. In other words, we can describe a hospital or a city using the same conceptual apparatus as a single neuron.

The Rejection of Linear Determinism

For a long time, the primary framework of neurophysiology and its application to other sciences was the reflex—the "stimulus-response" mechanism. There is a stimulus, followed by a response; it appears simple and elegant. This picture accurately describes very brief and primitive reactions, but it collapses the moment we attempt to explain complex actions: planning, learning, errors, shifts in strategy, and long-term goals.

The reflex arc is always a response to what has already occurred. It depicts the organism as something that "waits for an impact" and then reacts. In such a model, there is no place for the future—only for a reaction to the past.

Anokhin proposed a different way of viewing behavior. He argued that the stimulus is not the primary factor; the primary factor is the result toward which the system strives. Instead of the linear "impact-reaction" line, he draws a closed functional ring: needs, motivation, context, memory, result prediction, action, result control, and correction. Thus, behavior is not an automatic reaction to the past, but a continuous verification of hypotheses about the future: "If I act this way, will I achieve the result the system requires to preserve itself?"

The Functional Ring and Self-Learning

We replace the reflex arc with the functional ring. At the center of this ring is the principle of "result-feedback." The system first forms an image of the result (what must happen to consider the action successful), and only then selects the means, acts, measures how closely the actual outcome matches the planned one, and corrects itself.

This implies that an intellectual act is not a "one-off response," but a multi-stage cycle of self-verification:

Action → Verification → Correction → New Action

Error here is not a failure, but fuel for learning. The discrepancy between the expected and the factual triggers a restructuring of connections. This is the very principle upon which the plasticity of the biological brain and the adaptability of the most advanced neural networks are based. In machine learning terms, this is the analogue of "reward/penalty": if the result is better than expected, the strategy is reinforced; if worse, it is modified.

TFS as an Algorithm for Natural Intelligence

In the digital era, this logic becomes particularly valuable. If we want to create agentic systems—programs, robots, or infrastructural "brains" capable of independent action—it is insufficient for them to merely react to commands or events. They require an internal mechanism that allows them to constantly verify their course: "Does this action lead to the result I must maintain?"

Modern Reinforcement Learning (RL) algorithms, predictive control models, and "adaptive critics" are all technical embodiments of the same idea: there is a model of the world, a target result, an action, an error between the expected and the obtained (in the form of a reward or penalty), and an update to the strategy.

In the language of TFS, this cycle is structured as follows:

1. Afferent Synthesis: The collection and analysis of all available information (internal and external).
2. Decision Making: Choosing a single course of action.
3. Acceptor of Action Results: The creation of a predictive model of the result—an internal "standard."
4. Efferent Synthesis: The implementation of the action.
5. Return Afferentation: Receiving data about the actual result.
6. Correction: Modifying the action or the system itself based on the mismatch error.

Thus, TFS becomes the "stability algorithm" not only for biology but for Natural Intelligence as a whole—from an individual cell to the future Bio-Digital Noosphere. This hypersystem must similarly form a result image, act, measure deviation, and correct itself to preserve its integrity within a changing Universe.

You can learn more by reading our e-book 

Mykola Iabluchanskyi together with Andriy Yabluchanskiy