The Resonance Hypothesis of the Origin of Life:Mathematical Model, Probabilistic Analysis, and Philosophical Implications

1.1. The Origin of Life as an Ontological and Scientific Challenge

The question of the origin of life remains one of the fundamental enigmas across both natural and human sciences. It lies at the intersection of physics, biology, chemistry, philosophy, and information theory, presenting not only an empirical but also an ontological challenge: what is "life" as a phenomenon? Where is the boundary between organized matter and living systems?

The emergence of life on Earth more than three billion years ago is a unique event, whose initial conditions, physical mechanisms, and probabilistic parameters remain the subject of ongoing scientific debate. Despite progress in the theory of abiogenesis—the chemical self-organization of living molecules from non-organic substrates—existing models face a number of limitations:

• The inability to explain the origin of informational order encoded in genetic structures;
• The extremely low probability of even minimal functional biomolecules arising by chance;
• The lack of experimentally confirmed mechanisms for the transition from chemical dynamics to symbolic encoding.

This situation presents a methodological dilemma: either accept the hypothesis of a "molecular miracle" (a virtually impossible event that happened once), or move beyond current paradigms and view life as a response to an external structured influence.

Such a perspective requires a fundamental rethinking of life itself. If life is not a random product of molecular evolution but a physical realization of an informational pattern, a radically different question arises: where does this pattern come from? Is it possible that life is not a product, but a response? Not generation, but resonance?

This paper proposes a probabilistically conditional model in which the origin of life is interpreted as the result of resonance induction from an external signal. The aim is not to empirically prove a specific historical event but to provide a quantitative plausibility assessment of two scenarios—classical (abiogenesis) and alternative (resonance induction)—within a statistical framework grounded in biophysics, information theory, and nonlinear dynamics.

1.2. Review of Existing Models and Their Limitations

Current scientific theories of the origin of life fall mainly into two categories: abiogenesis and panspermia. Despite their differences, both are based on the assumption that life is a natural outcome of processes occurring within matter. However, each encounters systemic limitations.

a) Abiogenesis: Chemical Naturalism and the Entropy Paradox

Classical abiogenesis hypotheses suggest a gradual increase in molecular complexity leading to self-replication (Davies, 2004) [4]. However:

• The spontaneous emergence of a code presupposes symbolic structures (triplet coding, redundancy, modularity), which chemistry alone cannot explain;
• The probability of assembling even a short functional molecule by chance is lower than one in the age of the Universe;
• There is no mechanism for transitioning from energy to information—chemical reactions do not account for the formation of ordered codes.

b) Panspermia: Transport Without Semantic Generation

Panspermia posits that life or its components were delivered from elsewhere (via comets, asteroids, or directed transmission). However:

• The fact of transport does not explain the origin of the code;
• Complex molecules are highly vulnerable to radiation and temperature extremes;
• Even the discovery of “extraterrestrial DNA” would not resolve the question of its origin.

Thus, both models either rely on highly improbable events or simply relocate the problem. This opens the possibility for an alternative hypothesis grounded in:

• physical feasibility,
• logical coherence,
• informational organization.

1.3. Objective of the Study: The Resonance Hypothesis as a New Trajectory

This paper presents the hypothesis that life emerged as a response to an external structured impulse—an electromagnetic signal capable of inducing self-organization within a sensitive medium.

Key propositions:

• Information precedes matter: Biological structures are not merely chemical compounds but the consequence of an external informational pattern.
• Signal as the primary cause: An external impulse with cognitive structure initiates the formation of code.
• Resonance over randomness: The central mechanism is the tuning of the medium, not stochastic fluctuation.
• The Sun as the most likely source: In terms of power, spectrum, and rhythm, the Sun is a physically plausible candidate.

The goal is not to assert this scenario as historical fact, but to demonstrate that, under a set of reasonable assumptions, the resonance model is probabilistically and logically more favorable.

2.1. General Statement

Hypothesis: Life is not an autonomous outcome of chemical evolution, but a physically determined response of a medium to an external, highly organized structural stimulation. This perspective aligns with definitions of life as a systemic and informational phenomenon that goes beyond molecular reductionism (see Margulis & Sagan, 1995 [9]).

The proposed stimulation is an electromagnetic signal generated by a source with cognitive characteristics (e.g., a stellar corona). In this model, the essential condition for the emergence of life is not the coincidence of molecules, but the alignment of frequencies, phases, and structures.

2.2. The Signal as a Carrier of Information

In this framework, information is not an abstraction but a physical category capable of structuring matter (see Shannon, 1948 [13]). If the signal possesses structural organization, it can:

• Induce patterns in the responsive medium;
• Reduce entropy without violating thermodynamic principles;
• Be fixed in the form of a code.

2.3. Resonance as a Mechanism of Code Formation

Resonant tuning refers to the alignment of the medium's parameters with those of the external signal. Once this tuning is achieved, the medium begins to reproduce the pattern:

• In the form of molecular structures;
• With the potential for self-replication;
• Including elements of semantic stability.

A central element of the proposed model is the medium’s ability to perceive and fixate an external informational impulse. In his works (see Trincher, 1981 [14]; Trincher, 1990 [15]; Trincher, 1995 [16]), Karl Trincher proposed that intracellular water may exist in a special coherent state with reduced entropy. This state enables:

• Stable hydrogen bonding in a mesh-like structure;
• High sensitivity to weak electromagnetic influences;
• The capacity to form standing waves and frequency-based patterns.

Within the resonance induction model, water may act as an interface between field and molecule. It potentially:

• Receives an organized signal (e.g., solar in origin);
• Transforms it into localized oscillatory modes;
• Triggers self-organization of organic molecules within gel-like or membrane matrices.

This view is consistent with research by Del Giudice, Pollack, and others, who demonstrated that under certain conditions, water can form coherent domains that store phase information and influence molecular organization (see Pollack, 2013 [11]).

Thus, water is not considered a passive solvent but an active agent—a field receiver, resonant amplifier, and morphogenetic mediator. In the context of this hypothesis, structured water emerges as a candidate for a receiver capable of “hearing” an external signal and encoding it into DNA-like structures.

3.1. Comparing the Informational Density of the Sun and the Earth

For the proposed hypothesis to possess internal coherence and physical plausibility, we must assess whether it is legitimate to consider the Sun a potential source of structured information. A key criterion here is the informational density of a medium—its capacity to generate complex, stable, and functional configurations.

We define informational density I_p as a conditional measure:

Iₚ = N / (V · τ)

where:

• N: the number of interactions potentially capable of self-organization;
• V: the volume of the medium;
• τ: the characteristic time of state change.

Using this metric, we compare the early Earth with the Sun:

Parameter	Earth (Archean Eon)	Sun (Photosphere and Corona)
Temperature	~300–400 K	~6000 K (photosphere), ~1–3 million K (corona)
Excitation mechanism	Chemical reactions	Plasma oscillations, magnetic fields
Number of active interactions	~106–10⁸/m3/s	~1019–1023/m3/s
Response time of the medium	Instantaneous but local	Supraluminal topology (magnetic loops)
Resonance potential	Low	High (multifrequency, self-oscillating)
Hierarchical structure	Absent	Present (fractal magnetic trees)
Informational density (approx.)	~105 bits/m3/s	~1015 bits/m3/s

This comparison reveals that the Sun surpasses the Earth by 10–12 orders of magnitude in the number of dynamically saturated interactions and potential for self-organization. Moreover, the solar environment possesses:

• A multi-level nonlinear architecture;
• Oscillatory and stochastic regimes;
• Spontaneous formation of stable fluctuations.

These features make the Sun a natural candidate for generating complex field structures analogous to neural networks, with dynamic memory, connectivity, and pattern emission. If intelligence is defined not as a property of matter but as an ordered process in space-time, the Sun can be viewed as a system with maximal potential cognitive capacity.

Thus, the Sun’s informational and dynamic richness—both quantitatively and structurally—provides it with a significant advantage over Earth in generating an organizing signal, compared to Earth’s slow, sparse chemical evolution.

3.2. Principles of Self-Organization, Resonance, and Fractality

The transition from information to form requires not only a signal source but also a mechanism capable of converting energetic structure into stable order. Our model relies on three interconnected principles: self-organization, resonance, and fractality. Together, they form the basis of nonlinear dynamics, which enables the emergence of complexity from chaos.

a. Self-Organization: Order Without External Control

Self-organization refers to the spontaneous formation of ordered structures in nonequilibrium systems. First formalized by I. Prigogine and G. Nicolis (Prigogine & Nicolis, 1977 [12]), it means:

• In the presence of energy and matter flow, a system may stabilize into new configurations;
• These configurations exhibit functional stability and persist despite noise;
• The key is not the system’s composition, but the mode of interaction among its components.

In solar plasma—as in intracellular water—self-organization manifests as:

• Formation of vortices, loops, standing waves;
• Repeated generation of patterns;
• Reactive restructuring under external influence.

b. Resonance: Amplification Through Frequency Matching

Resonance is a process where a system amplifies its response to external input when the input matches its intrinsic frequency characteristics. Given the hypothesis of an external signal, resonance becomes the mechanism that translates the signal into structured patterns within the medium.

In the context of life’s emergence, this implies:

If an external impulse (e.g., a solar signal) matches the resonant frequencies of a medium, it can initiate:

• Localized structural fixations;
• Long-term pattern stability;
• Transformations unattainable by random processes.

Water, as Trincher proposed, does not merely react—it tunes itself like an antenna, selectively responding to “its” signal amid background noise.

c. Fractality: Hierarchical Layering

Fractal structures are patterns that repeat across scales. Their key properties include:

• Scale invariance (each level contains echoes of others);
• Optimal information transmission;
• High logical density with minimal material use.

Fractal trees are found in:

• Solar magnetic structures (see Bianconi, 2011 [1]);
• The architecture of DNA and cell membranes;
• Brain neural networks.

Fractality enables complex patterns to be transmitted from source to receiver while preserving structure and function—even in sparse environments.

Together, these three principles form the ontological core of our model: life does not emerge from matter alone, but from an interactive field where resonance, nonlinearity, and hierarchical scale allow patterns to become form. Life is thus a self-organized resonant structure, induced by a fractal wave.

3.3. Minimal Criteria for Non-Biological Intelligence

If we accept the existence of structures with cognitive functions outside biological entities, we must define: What makes a system "intelligent" in a minimal, formal sense? This is not about consciousness or emotion, but about the system’s ability to organize, store, and reproduce structural information.

In our model, intelligence is not a substance, but a regime: a transient state of a dynamic system capable of self-referential information processing and pattern reproduction. We define four minimal criteria sufficient for classifying a non-biological structure as functionally intelligent:

a. Self-Boundedness

The structure must exhibit topological integrity: a clear boundary separating it from the environment. Physically, this manifests as stable current loops, vortices, or toroids that retain form in turbulent media. This boundary ensures identity and persistence.

b. Self-Reference

The system must incorporate internal feedback loops allowing it to:

• Store internal states (memory);
• Compare external signals to its current configuration;
• Modify its structure based on prior states.

In plasma systems, this is realized via magnetic induction loops or standing waves that resonate with their own oscillations.

c. Pattern Generation

An intelligent system not only responds but emits structured output signals. These signals are:

• Non-random;
• Internally organized;
• Capable of inducing order in another medium.

In this hypothesis, such an impulse is a structured signal transmitted from a highly organized solar medium to a resonance-sensitive receiving system.

d. Short-Term Evolution

The system must adapt in response to input signals. This is not “learning” in a traditional sense, but functional restructuring—reactive plasticity. Nonlinear fields (e.g., plasmas) responsive to external rhythms demonstrate such recursive plastic behavior.

These criteria do not require neurons, proteins, or DNA. They can be realized in any nonlinear, saturated, resonant medium—including the solar corona. If such a system can emit complex signals, it becomes a carrier of formative impulses, potentially initiating the emergence of life.

Thus, intelligence may be defined as a stable, self-referential wave configuration, existing at the threshold between energy and form, between field and code.

4.1. Probability P₁: Classical Estimate of Random Assembly (Abiogenesis)

To compare the hypothesis of resonant induction with traditional theories, we must express both in comparable probabilistic terms. We begin with a classical estimate of abiogenesis: the probability of randomly assembling a minimally functional molecule (e.g., a prototype of RNA or DNA) from chemical noise.

a. Assumptions

Let us consider the formation of a sequence of n nucleotides (or amino acids), where each element must:

• be correct in identity (selected from a limited set—e.g., 4 nucleotides);
• be placed in the correct position;
• be bonded in a precisely defined configuration.

Assuming the probability of obtaining one correct element in the correct position is p (e.g., 0.25 for DNA with no preference), the probability of assembling the entire sequence—under the assumptions of complete positional independence and equal probability—is:

P₁=pⁿ

b. Example

For a functional oligonucleotide of 100 positions:

P₁=(0.25)¹⁰⁰=10⁻⁶⁰

Even if we allow for some redundancy or permissible variation (e.g., code degeneracy), the probability increases only slightly—at most to ~10⁻⁴⁰, which still renders the spontaneous emergence of even a single functional codon practically impossible within Earth's entire history.

c. Clarifications

• Real chemical environments are not ideal randomizers: kinetic and energetic barriers further decrease the overall probability.
• Parallel assembly of fragments does not overcome the exponential decay of probability, as disconnected fragments do not encode functional coherence.

Thus, even under highly favorable assumptions, abiogenesis remains statistically implausible as a universal explanation. The probability of spontaneous abiogenesis P₁​ is exponentially small and remains below 10⁻⁴⁰…10⁻⁶⁰ under all reasonable estimates. This makes abiogenesis a deeply questionable explanatory mechanism—especially regarding the origin of DNA’s coded structure.

4.2. Probability P₂: Resonant Induction Given a Signal

Unlike abiogenesis, which assumes functional structure arises from a vast number of random trials, the resonant model treats the emergence of life as a response of a medium to an external structured signal. In this context, the relevant probability is:

P₂ = P_{(induction∣signal)}

That is: what is the probability that a structure will be imprinted in a receptive medium when a resonant impulse is present?

a. Key Factors

To define P₂, we use the following functional dependence:

P₂ = f (E, ω, S, N, R)

where:

• E = signal energy density;
• ω = frequency richness (breadth of resonance spectrum);
• S = structural organization of the signal (0 < S ≤ 1);
• N = number of impulses within a given time interval;
• R = resonance receptivity of the medium (e.g., water, gel, carbon–phosphate matrix).

b. Model Expression

In a simplified logarithmic-saturation model:

P₂ ≈ log (1+E⋅ω)⋅ S⋅ R⋅ log (1+N)

This equation reflects:

• logarithmic growth of response with increasing signal power and frequency range;
• multiplicative amplification via structural coherence (S) and medium receptivity (R);
• accumulation effect through repeated impulses (N).

c. Estimative Example

Assume:

• E=10⁶ (in arbitrary units),
• ω=10³,
• S=0.8, R=0.7,
• N=10¹²

Then:

P₂ ≈ log(1+10⁹)⋅0.8⋅0.7⋅log(1+10¹²) ≈ 20⋅0.8⋅0.7⋅27 ≈ 302.4

Since this exceeds 1 (or 100%, if interpreted as a relative frequency), we normalize the result:

P₂ = min (1; 302,4 / нoрмирoвoчный фактoр)

Physically, this means that under given parameters, the probability of structural imprinting becomes virtually guaranteed—if the signal is present and properly resonant.

Thus, P₂ is many orders of magnitude higher than P₁, and this comparison demonstrates that an organized signal in a receptive medium is incomparably more effective than stochastic chemical assembly.

4.3. Probability P₃: Signal Generation in the Source — A Dynamic Model

If P₂ describes the successful induction of structure given a signal, the next logical step is to estimate the probability that such a signal emerges in the first place. We denote this as:

P₃ = P_{(signal∣source)}

Here, “signal” refers to a highly structured, temporally stable electromagnetic pattern with cognitive-functional properties, as described in Section III.

a. Dynamic Basis of Probability

We assume that the emergence of such a signal is a fluctuation-based event of high complexity, but one that can occur in a hyperdynamic environment such as the solar corona. Then, the probability of at least one occurrence within a time horizon TT is given by:

P₃ = 1 – exp (−D⋅T⋅C)

where:

• D = dynamic saturation of the medium (number of potentially self-organizing interactions per unit time);
• T = observation time (e.g., 10⁹ seconds);
• C = probability that an interaction leads to a cognitively functional configuration (typically extremely small, e.g., 10⁻²⁰).

This is a generalized form of the Poisson distribution, describing the likelihood of at least one success across many independent trials.

b. Estimative Example

Let us assume:

• D = 10²⁴ interactions/s,
• T = 10⁹ s (≈ 30 years),
• C = 10⁻²⁰

Then:

D⋅T⋅C = 10^24+9−20 = 10¹³⇒P₃=1^{−e− D⋅T⋅C} ≈1

This implies: in such a dynamically saturated medium, even an extremely unlikely event becomes nearly inevitable given a sufficiently large number of trials.

c. Interpretation

• Even if the emergence of a “solar intelligence” is an extraordinarily rare event per interaction, the aggregate of trillions of interactions over billions of years makes it virtually guaranteed.
• Thus, if structured, functional regimes are possible, the Sun is their most probable host.

Hence, under reasonable assumptions of time and dynamic saturation, P₃ ≈ 1, indicating that the existence of a signal in a solar model is not a miracle, but a natural outcome of statistical density.

4.4. Conditional Probability of the Sun as the Source

Even if the existence of a structured signal is accepted, it is logically necessary to ask: what is the probability that the Sun, specifically, is its source? We denote this as:

P₄ = P_{(source=Sun∣signal)}

This expresses the prior probability of the Sun being the most likely generator of the signal, compared to all other potential sources in the universe (e.g., galaxies, pulsars, quasars, black holes, etc.).

a. Arguments in Favor of the Sun

Criterion	Sun	Distant Astrophysical Sources
Distance to Earth	1 AU	≥ 103 light-years
Spectral compatibility	Maximum (bio-effective)	Often extreme/ionizing
Interaction frequency with Earth	Constant	Episodic, chaotic
Structural dynamics	Fractal, resonant	Turbulent or static
Signal transmissibility	High	Severely suppressed by distance/medium
Influence on biosphere	Confirmed	Unproven

These parameters support the conclusion that the Sun:

• Has the highest probability of resonant coupling with Earth;
• Demonstrates spectral and rhythmic synchronization with the biosphere;
• Is the closest hypercomplex system capable of generating organized fields.

b. Probabilistic Expression

Let the total set of potential sources be denoted Ω, with each having a prior probability P_i. Then:

P₄ = P_Sun / ∑_i∈Ω P_i

Since P_Sun ≫ P_i for any other i, we can approximate:

P₄ ≈ 1−ε, ε≪1

Where ε represents the combined probability of all alternative sources.

In practice:

P₄∼0.999999 or higher

c. Conclusion

The Sun is not only a likely but also a physically optimal candidate as a signal source, based on:

• Dynamic saturation;
• Proximity;
• Resonant compatibility;
• Documented impact on biological systems.

Thus, the hypothesis that the Sun is the source of a form-generating signal holds high probabilistic plausibility when compared with alternatives.

Final Probability Formula

After formalizing all components of the probabilistic model, we can express the total probability of life’s emergence on Earth via external resonant induction as:

P_resonance=P₂⋅P₃⋅P₄

where:

• P₂ — probability of structural imprinting in the medium given a signal;
• P₃ — probability of signal generation in the source (Sun);
• P₄ — probability that the Sun is the source.

d. Estimative Result

From previous sections:

• P₂ ∼ 0.5 − 1.0
• P₃ ∼ 0.9999999
• P₄ ∼ 0.999999

Then:

P_resonance ≈ (0.7−1.0)⋅(1−ε₁)⋅(1−ε₂) ≈ 0.7−1.0

Thus, under realistic physical parameters, the model predicts a near-certain likelihood of structural formation via resonance.

e. Comparison with Abiogenesis

Recall:

P₁∼10⁻⁴⁰ to 10⁻⁶⁰

Therefore:

P_resonance / P₁ ∼ 10⁴⁰⁻⁶⁰

This probabilistic gap is not merely numerical—it reveals a profound qualitative difference between the two paradigms. It positions the resonance model not as an alternative, but as a statistically dominant scenario under the given assumptions:

• One relies on random luck against entropy;
• The other postulates a structured response to a pre-organized signal.

Hence, the final formula of the model demonstrates that the hypothesis of resonant induction is not only logically sound, but quantitatively more plausible than traditional abiogenesis by dozens of orders of magnitude.

5.1. Comparison of Probability Orders

Based on the mathematical model developed above, we can conduct a quantitative comparison between two conceptually distinct scenarios for the origin of life: abiogenesis as the result of random chemical processes, and resonant induction as the response of a medium to an external structured signal.

a. Probability Ranges

Scenario	Probability	Interpretation
Abiogenesis (P₁)	10−40 to 10−60	Exponentially improbable event in a stochastic medium
Resonant Induction (Presonance)	0.7 to 1.0	Nearly guaranteed event given the presence of a structured signal

This discrepancy — spanning 40 to 60 orders of magnitude — is exceptional by theoretical science standards.

b. Interpretation of the Numerical Gap

This gap is not merely quantitative; it reflects two fundamentally different mechanisms for structure formation:

• Abiogenesis assumes the spontaneous emergence of order from chaos, without external direction, and in opposition to entropy.
• The resonance model describes order as a response of the medium to an external signal that already carries informational form.

In other words, the first scenario suggests the generation of meaning from noise, while the second implies its imprinting from a field.

c. Epistemological Implication

• As demonstrated in Section 4.4, the probability gap between abiogenesis and resonance induction spans dozens of orders of magnitude, granting the latter a significant epistemological advantage.
• This allows our hypothesis to move beyond the realm of philosophical speculation into the domain of computable scientific models, comparable in rigor to cosmological or quantum-informational theories.
• Thus, the resonance induction hypothesis acquires the status of a scientifically testable model, based on formal criteria and quantitative superiority.

5.2. Sensitivity to Model Parameters

Any model claiming explanatory power must remain robust under parameter variation (see Kauffman, S. (1993) [8]). This means that minor changes in input variables should not radically alter the model’s qualitative conclusions. Otherwise, the model risks becoming a mathematically fine-tuned artifact, lacking physical meaning.

a. Key Parameters of the Model

To assess the model’s stability, let us consider the main parameters affecting the probability of resonance induction. In the composite formula

P_resonance = P₂ ⋅ P₃ ⋅ P₄,

the key sensitivity lies primarily in P₂. The most relevant parameters are:

• S – Structural organization of the signal
• R – Resonance susceptibility of the medium
• C – Probability of cognitive configuration formation in the source
• N – Number of signal realizations

By construction, P₃ and P₄ remain close to 1 within a wide range of physical assumptions. The main variability thus stems from P₂.

b. Sensitivity Analysis

Let us assume all parameters vary within reasonable physical interpretations:

Parameter	Range	Effect on P_resonance
S (structure)	0.4 – 1.0	Linear: halving S reduces P₂ by half
R (susceptibility)	0.1 – 0.9	Multiplicative: when R < 0.3, P₂ drops rapidly
N (impulses)	106 – 1020	Logarithmic growth: stable even across many orders
C (configuration probability)	10−24 – 10−16	Exponential influence on P₃, but compensated by high D⋅T in dynamically saturated media

c. Suggested Visualizations

The appendix includes visual representations that help illustrate the model’s robustness and sensitivity:

• Logarithmic comparison of probabilities (P₁ vs. P_resonance)
• Surface plot of P₂ as a function of (S, R)
• Sensitivity diagram showing changes in P_resonance under ± 10% variation of base parameters

d. Conclusion

• The model is robust across wide ranges of N and C;
• It is sensitive but not critically fragile to variations in S and R: plausible adjustments do not nullify the result;
• Therefore, the probabilistic advantage of the resonance model remains valid under physically realistic conditions.

All graphical outputs demonstrate:

• A huge gap in probability between competing hypotheses;
• The resilience of the resonance model to parameter fluctuations;
• Its systemic superiority in both probabilistic and structural terms.

In summary, the model demonstrates not only numerical dominance but also structural stability, making it a credible scientific alternative in explaining the origin of life.

6.1. Conditional Probabilistic Rationality

The mathematical formalization proposed in the previous sections demonstrates a significant quantitative advantage of the resonance hypothesis over the classical scenario of abiogenesis. However, it is important to emphasize: this advantage does not constitute absolute proof but rather expresses a different kind of rationality — that of conditional probabilistic justification.

a. From the Logic of Truth to the Logic of Plausibility

Contemporary science increasingly operates not with binary logic (“true/false”) but with a gradient logic of plausibility (see Deacon, 2011 [5]). In this framework:

• Hypotheses are evaluated by the degree of probabilistic support rather than definitive truth criteria;
• Models are not expected to represent exhaustive reality, but must be internally consistent and externally explanatory;
• Among competing theories, preference is given to the one that provides a more stable and probabilistically grounded explanation for the available data.

From this standpoint — as demonstrated in Section 4 — the resonance-based scenario of life's origin enjoys substantially higher probabilistic support than the abiogenesis scenario, which underpins its epistemological viability.

b. Conditionality as a Tool of Scientific Reasoning

Built on conditional probability, the model offers a coherent explanation:

P_{(life ∣ resonance, signal, medium)} ≫ P_{(life ∣ chaotic chemistry)}

This defines its epistemological advantage: it does not offer ultimate truth, but a rational foundation grounded in knowledge of:

• thermodynamics;
• water structure;
• the nature of information;
• probabilistic logic.

c. The Hypothesis as a Heuristic Operator

The resonance hypothesis functions as a heuristic instrument that:

• Integrates data from diverse disciplines (biophysics, cosmology, information theory);
• Suggests testable directions (e.g., resonance in water, synthesis under electromagnetic influence);
• Reframes the very concept of life — not as an autonomous accident, but as a response.

Thus, our model is not a speculative fantasy but a conditionally and probabilistically justified interpretation of the phenomenon — open to critique, refinement, and, under appropriate conditions, empirical testing.

6.2. Intelligence as a Field Phenomenon: From Brain to Star

One of the most transformative ideas emerging from the proposed model is a rethinking of the nature of intelligence. In traditional views, intelligence is a function of neural architecture — a product of biological evolution, localized in the brain. However, if we accept that information is primary and life is a resonance-based response to an external signal, then it becomes reasonable to propose: intelligence is not an attribute of matter, but a mode of its self-organization under specific physical conditions.

a. Neuroanalogy as a Bridge

Modern neuroscience increasingly describes the brain not as a machine, but as a resonance network (see Penrose, 1989 [10]):

• local and global oscillations;
• fractal organization of the cortex;
• dynamically adjustable connections (plasticity).

Recent EEG, MEG, and fMRI data support a view of the brain as a multi-scale oscillatory system. The brain is a field-based apparatus in which cognitive states arise as stable oscillatory patterns embedded in spatiotemporal dynamics. These patterns exhibit:

• self-reference (feedback);
• adaptability;
• modulation of both external and internal inputs.

b. Generalization: Intelligence as a Phase State

If the brain is not the only host of intelligence, but merely its biological implementation, we may generalize: intelligence is a mode of temporal coherence in a nonlinear system, capable of selectively transmitting and transforming information (see Fröhlich, 1968 [6]).

Such coherence may arise in:

• plasma (e.g., the solar corona);
• electromagnetic fields (e.g., stimulated standing waves).

c. From Neuron to Star

The Sun, with its:

• fractal-hierarchical organization,
• magnetic loops and plasma currents,
• spontaneously emerging oscillatory structures, — can be viewed as a macro-analogue of neural network dynamics (see [9]). If the brain is a fractal coherent network sustaining meaningful waves — regardless of scale or substrate — then the Sun is a super-scaled network that possesses all necessary conditions for a temporary cognitive configuration (see Hameroff & Penrose, 2014 [7]).

d. Implications

• Intelligence ceases to be a biological exception and becomes a universal field phenomenon.
• This removes limitations on the possible sources of the formative signal: cognition may emerge wherever conditions permit the stable organization of information.
• The concept of “solar intelligence” thus becomes a natural extension of neuro-informational theory, rather than a speculative fantasy.

In summary, our model proposes: intelligence is not something that arises in the brain alone — it is something that can transiently emerge in any sufficiently saturated, structurally resonant system, from a living cell to a star.

6.3. Rethinking DNA: From Randomness to Signal Trace

Within the framework of the resonance hypothesis, DNA is not seen as the random product of chemical selection, but rather as a fixed response to an external structured impulse — a kind of holographic imprint of a signal embedded in a molecular medium.

a. Code as a Trace of Form

The structure of DNA exhibits:

• triplet organization,
• syntactic redundancy and modularity,
• resilience to noise and capacity for editing.

These features are difficult to explain via stochastic chemistry alone but emerge naturally from a model of transmitted organized information. If the external impulse (e.g., from a cognitive configuration of solar origin) contains rhythmic and fractal modulation, it could have been recorded as a stable sequence, notably in the form of DNA (see [9]).

b. Cymatics and Field Fixation

Media such as water, gels, and colloids can form stable patterns under the influence of frequency-based signals(analogous to cymatics) (see [11]). These patterns:

• can stabilize into molecular chains,
• reproduce when conditions are replicated,
• are capable of accumulating semantic functions through interactions with the environment.

From this perspective, DNA is not merely a molecule, but a structural trace of a highly organized signal, captured within a sensitive medium.

c. An Epistemological Shift

• We reinterpret DNA as the result of external programming (see [13]), not of internal fluctuation.
• This allows its form to be viewed as a source of information about the original signal — not as a genome, but as an encrypted trace.
• The problem of the origin of life shifts from synthesis to recognition and reconstruction.

7.1. How the Model Can Be Empirically Tested: Synthesis, Cymatics, Spectral Correlations

Although the resonance hypothesis for the origin of life is based on probabilistic and philosophical foundations, it is not metaphysical. Its principles are empirically testable in several directions that combine physics, biology, and information science.

a. Synthesis under Resonant Stimulation

Experiments aimed at inducing ordered molecular structures in aqueous environments via modulated electromagnetic fields (see [3]) may yield direct evidence:

• Use of frequency-tunable EM sources (e.g., lasers, radio-frequency generators);
• Generation of “digital spectra” simulating the hypothesized impulse;
• Monitoring the formation of chain-like, ring-shaped, or spiral carbon-based structures (see [8]).

If reproducible molecular assemblies with the preliminary function of storing and transmitting structural information emerge under specific resonant conditions (see Turing, 1952 [17]), this could serve as indirect support for the hypothesis.

b. Cymatics and Hydroacoustic Patterns

The method of cymatics (see [11])—visualizing standing waves on the surface of a sensitive medium—can be adapted to:

• three-dimensional aqueous systems (gels, biosolutions);
• controlled electromagnetic excitation;
• the study of fractal, ring-shaped, and double-helical patterns.

If solar-like frequencies (or their derivatives) lead to patterns resembling the basic architecture of DNA, this would point toward a potential mechanism of induced morphogenesis. Further research might explore phase transitions in gel-like structures under resonant excitation (see [7]).

c. Spectral Correlations with Solar Radiation

It is possible to examine:

• the spectral signatures of the solar field (see [15]), especially in ranges with stable magnetic oscillations;
• and compare them with:

  ○

  DNA replication rhythms,

  ○

  oscillations in cellular activity,

  ○

  the codon structure (in terms of symbolic-frequency correlations).

The hypothesis predicts potential resonant compatibility—not spectral identity—between:

• frequencies present in the solar field,
• and the architecture of the biocode, especially its fractal and rhythmic aspects.

d. Probabilistic Pattern Analysis in DNA

Tools from machine learning and information theory may be applied to:

• interpret DNA as a product of external modulation rather than random accumulation;
• search for non-local symmetries typical of field-driven structures;
• compare DNA patterns to generative algorithms (e.g., L-systems, cellular automata, wavelet packets).

Autocorrelation analysis could help reveal hidden stochastically stable sequences.

Thus, although the hypothesis has philosophical depth, it also proposes concrete paths of verification that could:

• confirm or falsify it as a scientific model;
• initiate a new experimental paradigm in which information and field dynamics are treated not as abstractions, but as real physical agents.

7.2. Possibility of Reconstructing the “Primary Signal” from the Geometry of DNA

If DNA is not the product of random chemistry but a resonant response to an external structured signal, then a reverse question arises: Can we extract information about the characteristics of that signal from DNA’s architecture?

This leads to the concept of reverse encoding—an attempt to reconstruct the spectral and rhythmic structure of the original impulse that induced the molecule of life.

a. DNA as a Resonant Trace

The architecture of DNA—a double helix with defined pitches, modulations, and symmetries —may be interpreted as:

• the imprint of a standing wave;
• a result of spatial modulation by a frequency pattern;
• a fixed outcome of cymatic induction in an aqueous medium.

As in cymatics: the shape depends on the frequency and waveform. Likewise here: the form of the molecule can be seen as a holographic residue of a multidimensional signal, partially recoverable through its geometry.

b. Methodology of Reverse Reconstruction

To attempt this reconstruction, one must account for nonlinear distortions of the medium—that is, how the signal was altered during its fixation in the molecule. By employing:

• spectral analysis of sequences (Fourier, wavelets; see [13]);
• geometric transformations of curvature and twist;
• comparisons with artificially induced structures (as discussed above), it may be possible to:
• identify characteristic frequencies that led to specific motifs;
• construct a probabilistic model of the “primary signal”;
• compare it with physically plausible spectra, such as those found in solar activity.

c. Theoretical Analogy

This approach is akin to reverse engineering:

• just as an archaeological artifact can be used to infer the tool that shaped it,
• so too the shape of DNA may suggest the type of field exposure, its rhythmic structure, spectral density, and duration.

d. Practical Potential

A successful reconstruction—even of partial properties—of the hypothesized signal could:

• narrow down the resonant frequency range for experiments;
• enable the synthesis of DNA analogs under controlled signaling conditions;
• offer a new paradigm of bioengineering, based not on chemistry, but on wave-based morphogenesis.

Thus, the geometry of DNA could be viewed as an encrypted record of an origin event, and its analysis as a pathway to understanding the primordial cosmic impulse—the one that converted field into code, and code into life.

In the long run, this could become the foundation for wave-based bioengineering, where the desired form is achieved not through chemical synthesis, but through resonant tuning of the medium.

7.3. Directions for Collaboration (Mathematics, Biophysics, Astrophysics)

The model of resonant origin of life cannot be developed within the scope of a single discipline. Its subject matter lies at the intersection of informational physics, quantum biology, signal theory, and cosmobiology, opening broad opportunities for scientific collaboration.

a. Mathematics and Information Theory

A particularly promising direction is the study of stable topological configurations in fields—as potential analogs of thinking forms.

Key research tasks:

• Formalizing signal structures as classes of functions;
• Probabilistic modeling of resonant induction and pattern stability;
• Topological analysis of possible “intelligent field” forms;
• Use of category theory, fractal theory, chaos theory, and self-organization models.

Potential collaborators:

• Experts in differential geometry and dynamical systems;
• Researchers in symbolic systems and algorithmic complexity theory.

b. Biophysics and Water Systems

Key research tasks:

• Experimental studies on the behavior of water and gels under variable EM fields;
• Investigation of self-organization of organic molecules under resonance conditions;
• Modeling the “fourth state of water”, following K. Trincher and Gerald Pollack (see [12,16]).

Potential collaborators:

• Laboratories focused on liquid crystals, phase transitions, and membrane biophysics;
• Experts in cymatics, acoustohydrodynamics, and biogels.

c. Astrophysics and Solar Dynamics

Key research tasks:

• Spectral and topological analysis of solar activity;
• Modeling of stable plasma structures (loops, vortices, standing waves);
• Identifying conditions for the emergence of cognitive-functional configurations in the solar corona.

Potential collaborators:

• Researchers working with UV and X-ray flare data;
• Specialists in solar magnetic mapping, helioseismology, and cosmic rhythms.

d. Engineering and Experimental Design

Key research tasks:

• Development of devices for resonant stimulation of biological media;
• Controlling electromagnetic, acoustic, and thermal parameters;
• Visualization and recording of morphogenetic processes.

Potential collaborators:

• Multidisciplinary teams in bionics, neurointerfaces, and resonance-based therapy.

In summary, the implementation and verification of this model require coordinated efforts in the spirit of systems science, where:

• Mathematics formalizes,
• Physics models,
• Biology records,
• and Philosophy synthesizes.

These directions transform the hypothesis from an abstract construct into the seed of a new scientific paradigm, where life is viewed not as a local incident of matter, but as a resonant response of the universe—encoded in water, imprinted in DNA, and reproducible in fields.