Abstract
Resonance is real. Synchronization is real. Neural oscillations matter. Noise can sometimes improve information processing. Biological brains exploit temporally structured activity at every scale from single cells to distributed networks. None of these points should be controversial.
The more difficult question is whether those facts justify a stronger conclusion: that resonance is not simply one mechanism employed by intelligent biological systems, but the foundational principle that makes intelligence itself possible.
I do not think the evidence currently supports that conclusion.
This article presents the opposing case to Anamnesis’s Why Resonance?, which argues that resonance should occupy the conceptual center of a theory of systems intelligence. My disagreement is not with the importance of resonance. It is with the elevation of resonance from important mechanism to constitutive requirement.
The distinction matters.
Synchronization mathematics tells us how coupled oscillators coordinate, but it does not tell us why one synchronized state represents a face, another represents a prediction, and another represents nothing at all. Neural oscillations can causally affect cognition without being cognition itself. Stochastic resonance demonstrates that noise can improve signal detection in certain nonlinear systems, but it does not establish that physical noise is necessary for causal reasoning. Adaptive Resonance Theory demonstrates the power of recurrent matching and resonance-gated learning, but it does not prove that intelligence requires a particular oscillatory physical substrate.
Indeed, some of the strongest evidence for resonance cuts against an overly simple resonant theory. Too little synchronization can impair cognition, but so can too much. Epilepsy is an obvious reminder that coherence is not inherently intelligent. Oscillatory state-space models such as LinOSS can be implemented on ordinary digital computers, showing that resonant dynamics and digital computation are not mutually exclusive categories. Neuromorphic computing itself spans digital, mixed-signal, analog, event-driven, and stochastic implementations rather than defining a single alternative substrate.
My alternative position is therefore more conservative:
Intelligence is the capacity of an adaptive system to construct, revise, and act upon useful models of relationships in its environment. Resonance is one powerful family of mechanisms by which such systems may coordinate information through time—but coordination should not be confused with understanding.
Resonance may prove essential to some forms of biological intelligence. It may inspire superior artificial architectures. It may even turn out to be deeper than contemporary computational theory recognizes.
But that case has not yet been demonstrated.
The scientific opportunity is not to choose computation or resonance.
It is to discover what each actually contributes.
I. The Strongest Version of the Resonance Argument
Anamnesis begins from an observation I largely share: contemporary theories of artificial intelligence are heavily computational. Intelligence is typically framed as information processing—representation, transformation, optimization, prediction, inference, memory, search.
The biological brain, meanwhile, does not look like a conventional digital computer.
It is asynchronous.
It is noisy.
It is recurrent.
It is massively distributed.
Its components operate on overlapping temporal scales.
Its activity oscillates.
Its signals synchronize, desynchronize, phase-lock, burst, entrain, interfere, propagate, and reorganize.
These are not trivial differences.
Recent experimental evidence has strengthened the case that neural synchronization can play causal rather than merely correlational roles. A 2025 Nature Communications study, for example, reported causal evidence that gamma-band synchronization in visual cortex contributes to effective information processing and behavior.
The broader communication-through-coherence literature likewise proposes that phase relationships regulate when neural populations can effectively exchange information, although reviews continue to emphasize that the exact causal mechanisms and scope of the theory remain unresolved.
The brain is therefore not simply performing computations while oscillations happen incidentally in the background. Timing itself participates in the computation.
That is an important insight.
Where I part company with the stronger resonant thesis is the next step:
Resonance participates in biological cognition
↓
Resonance is fundamental to biological cognition
↓
Resonance is constitutive of intelligence
↓
Non-resonant substrates cannot genuinely understand
Each arrow represents an additional claim.
The first is strongly supported.
The second is plausible in at least some domains.
The third is an open hypothesis.
The fourth is highly speculative.
The central danger is treating evidence for the first proposition as if it establishes the last.
It does not.
A mechanism can be causally important without being the essence of the phenomenon it helps produce.
Vision requires retinal photoreceptors, but photoreceptors are not the essence of intelligence.
Human reasoning depends on glucose metabolism, but glucose is not therefore the currency of thought.
Speech depends on pressure waves, but intelligence is not acoustics.
Biological cognition depends heavily on ion channels, neurotransmitters, membrane potentials, glia, vascular dynamics, gene regulation, and metabolism. None of these facts alone establishes a substrate-independent theory of intelligence.
Resonance deserves investigation precisely because it may be unusually important.
But importance is not identity.
II. The Kuramoto Model Explains Synchronization, Not Intelligence
The Kuramoto model is one of the most elegant models in nonlinear dynamics.
A population of oscillators with different natural frequencies, coupled strongly enough, can undergo a transition from incoherent motion toward collective synchronization. The Kuramoto order parameter provides a compact measure of global phase coherence.
This is powerful mathematics.
Kuramoto’s own 2026 retrospective emphasizes the enormous reach of synchronization theory and its roots in phase reduction and nonlinear dynamical systems.
But the Kuramoto model poses a problem for any attempt to identify resonance too closely with intelligence:
Kuramoto synchronization is extraordinarily generic.
Oscillators synchronize in systems that nobody would describe as intelligent.
Metronomes synchronize.
Chemical oscillators synchronize.
Power-grid generators synchronize.
Fireflies synchronize.
Mechanical systems synchronize.
Electronic oscillators synchronize.
Populations of mathematical phase variables synchronize.
A Kuramoto order parameter can tell us how aligned those oscillators are.
It cannot tell us what their collective state means.
That distinction is fundamental.
Imagine two oscillator networks with identical order parameter:
R = 0.91
One might encode the trajectory of a moving object.
One might represent a remembered face.
One might be an electrical test circuit.
One might be experiencing pathological hypersynchrony.
One might have no semantic interpretation whatsoever.
The value of R does not distinguish them.
That is not a weakness of Kuramoto theory. It was never designed to solve semantics.
It is a weakness only if we ask synchronization theory to carry explanatory weight it was not built to carry.
Intelligence requires more than coordination.
At minimum, an intelligent system appears to require distinctions resembling:
state;
memory;
expectation;
error;
relevance;
action;
consequence;
generalization;
counterfactual possibility;
some mechanism for revising behavior when expectations fail.
Synchronization may help organize these processes.
Synchronization itself does not specify them.
This becomes even clearer when we consider systems with competing or antagonistic coupling. Kuramoto-family models can produce traveling waves, partial synchronization, chimera states, clustered synchronization, antiphase relationships, and other collective regimes.
There is no general principle that says:
more synchronization = more intelligence
Nor should we expect one.
A sophisticated intelligent system probably requires carefully controlled mixtures of:
synchronization desynchronization segregation integration competition inhibition persistence reset
That is already much closer to the architecture of a brain.
Once we admit that, however, resonance ceases to be a single scalar foundation of intelligence and becomes one member of a richer dynamical vocabulary.
I suspect that is where the truth lies.
III. Coherence Is Useful — But It Is Not a Currency
One of the strongest propositions in Why Resonance? is that coherence is the currency of intelligence.
It is an evocative phrase.
I do not think it survives close examination.
Coherence is useful only relative to what is being coordinated, when it is coordinated, and what the system is trying to accomplish.
The brain does not pursue maximal coherence.
It pursues appropriate coordination.
That difference is enormous.
Consider epilepsy.
Epileptic networks are classically associated with excessive or pathological synchronization. Modern neuroscience complicates the old picture of seizures as nothing more than simple hypersynchrony, but abnormal synchronization remains an important feature of epileptic dynamics. Cognitive impairment is also associated with pathological disruption of coordinated network interactions.
If coherence were literally the currency of intelligence, hypersynchrony ought to make the brain extraordinarily intelligent.
It plainly does not.
The better interpretation is that healthy cognition requires structured metastability.
Some populations synchronize.
Others decouple.
Some rhythms gate others.
Some regions transiently align and then disengage.
Information may require local synchrony and global differentiation simultaneously.
The system continually moves between integration and separation.
A choir is useful because singers synchronize certain properties while remaining distinct in others. If every singer produced the exact same waveform at maximum amplitude, the result would not be richer music. It would be one very loud note.
Brains appear to operate similarly.
Recent work continues to reveal oscillatory coordination at multiple scales, including coupling of neural activity to something as fundamental as respiration. A 2025 Nature Reviews Neuroscience article reviews evidence that breathing rhythms coordinate activity across numerous brain regions, including areas involved in cognition and emotion.
This tells us something profound about biological timing.
But it also warns against overinterpretation.
If respiration modulates cognitive networks, should breathing become the fundamental principle of intelligence?
Obviously not.
The correct lesson is that intelligence exists inside an embodied dynamical system in which timing matters everywhere.
That is different from saying one particular type of timing relation explains intelligence.
The phrase I would substitute is:
Coherence is one control variable of intelligent dynamics.
That claim is less poetic.
I also think it is more likely to be correct.
IV. Resonance Coordinates Information; It Does Not Explain Meaning
This is, for me, the deepest conceptual problem.
Suppose resonance successfully explains how distributed populations of neurons coordinate.
We still have to explain why those coordinated states refer to anything.
Consider a population of neurons that becomes phase-aligned while an animal sees a predator.
Another population becomes phase-aligned during the recollection of food.
Another becomes phase-aligned while planning an escape route.
Why does one coordinated pattern mean predator, another food, another left turn?
The phase relationships alone cannot answer that.
Meaning depends upon a history of relationships between:
internal state sensory evidence memory action environmental consequence
A system acquires meaning because its internal differences become reliably connected to differences in the world and to differences in what happens when it acts.
That is fundamentally relational.
Resonance may stabilize those relationships.
It may select them.
It may bind them.
It may regulate when they are learned.
But the content comes from the structure of the system’s interaction with the world.
This is why I am reluctant to accept the proposition that computation without resonance merely manipulates meaningless symbols.
Computational models can represent relational structure.
A map encoded digitally can preserve adjacency.
A simulator can represent causal laws.
A planning algorithm can evaluate counterfactual trajectories.
A learned world model can predict how interventions change outcomes.
Whether those representations qualify as “understanding” is philosophically difficult.
But dismissing them because they are computational does not solve the problem.
It changes the definition.
If we define genuine understanding as:
whatever biological resonance uniquely produces,
then of course digital systems cannot understand.
But the conclusion is built into the premise.
A stronger scientific approach would identify behavioral or internal criteria that distinguish:
correlation prediction causal model counterfactual reasoning transfer explanation intervention
and then test competing architectures.
If resonance-native systems consistently outperform non-resonant systems on those criteria, especially under controlled resource budgets, then the argument gets stronger.
Until then, “resonance creates meaning” is not an established mechanism.
It is a hypothesis.
V. Stochastic Resonance Does Not Establish a Digital Ceiling
Stochastic resonance is one of the most beautiful phenomena in nonlinear science.
Noise can improve the detectability of weak signals under the right conditions.
That result is genuine and has been studied in physical and biological systems for decades.
But the phrase “under the right conditions” is doing a great deal of work.
Stochastic resonance does not mean:
noise is always beneficial
It means there exists a regime in certain nonlinear systems where a non-zero noise level improves a selected response measure.
Too little noise may fail to help.
Too much noise degrades the signal.
Even contemporary applications of adaptive stochastic resonance emphasize this limitation: once environmental noise exceeds an optimal regime, additional noise worsens performance.
So I disagree with the broader proposition that noise is “the medium” of intelligence.
Noise can be a resource.
It can also be destructive.
What matters is the system’s ability to exploit uncertainty.
And that brings us to the digital-computation question.
The argument that deterministic digital computers cannot exploit stochastic resonance because they are deterministic confuses at least three levels:
physical substrate algorithm modeled dynamics
A digital machine can run a stochastic algorithm.
It can draw from physical random-number generators.
It can receive noisy sensors.
It can simulate stochastic differential equations.
It can approximate nonlinear threshold systems.
It can implement oscillator networks.
It can use probabilistic sampling.
And it can connect to analog or stochastic peripherals without ceasing to be fundamentally digital in its primary computational architecture.
Even entirely deterministic dynamical systems can exhibit extremely rich, chaotic behavior.
Reservoir computing has demonstrated the ability to learn and forecast complex nonlinear systems, including chaotic dynamics.
None of this proves that simulation is physically equivalent to biological stochastic resonance.
But that is precisely the point.
The equivalence or nonequivalence must be demonstrated.
It cannot simply be assumed.
There is a legitimate research question here:
Does substrate-native stochasticity produce capabilities that cannot be reproduced efficiently—or at all—by digitally simulated stochastic dynamics?
That would be a fascinating experiment.
What I reject is replacing that experiment with a philosophical declaration that digital systems necessarily face a qualitative ceiling.
We do not know that.
VI. Adaptive Resonance Theory Is Powerful — But It Does Not Settle the Substrate Question
Stephen Grossberg’s Adaptive Resonance Theory deserves serious attention.
ART addresses the stability-plasticity dilemma: how can a learning system acquire new categories without catastrophically erasing what it has already learned?
In ART, sufficiently good matches between bottom-up input and top-down expectation can produce resonant states that amplify and sustain activity and support learning. Grossberg’s own description emphasizes reciprocal matching, attention, synchronization, and learning stability.
This is one of the strongest pieces of the resonance argument.
But I think it is often interpreted too broadly.
ART shows that a particular recurrent dynamical architecture using resonance-like matching is capable of solving important learning problems.
That does not establish:
all learning requires ART
or:
all intelligence requires the same physical resonance mechanism
There is an important conceptual distinction between “resonance” as used in nonlinear physics and “resonance” as used in a cognitive architecture.
In ART the concept involves mutually reinforcing bottom-up and top-down activation that amplifies, synchronizes, and prolongs matched states. Grossberg explicitly connects these states to oscillatory synchronization.
So this is not merely a linguistic metaphor.
But neither is it equivalent to saying the Kuramoto order parameter is the governing variable of ART.
These theories operate at different descriptive levels.
The danger is semantic compression:
physical resonance neural synchrony ART resonance semantic agreement social resonance quantum coherence
all gradually become treated as manifestations of one underlying thing.
Maybe they are.
But sharing a word is not evidence of shared mechanism.
A useful scientific test is to remove the vocabulary entirely.
Instead of asking:
Is this resonance?
ask:
What variables interact, by what equations, over what timescale, producing what measurable outcome?
If two phenomena reduce to materially similar dynamical structures, then unification is earned.
If not, “resonance” may be functioning as analogy.
Analogies are useful.
They should not be mistaken for mechanisms.
VII. Digital Systems Can Already Be Oscillatory
Another reason I reject a sharp divide between “resonant intelligence” and “digital AI” is that these categories are already overlapping.
LinOSS is an obvious example.
Linear Oscillatory State-Space models explicitly construct sequence-processing dynamics from forced harmonic oscillators, yet they run perfectly well as mathematical models on ordinary digital hardware. LinOSS has demonstrated strong long-sequence performance, including nearly 2× Mamba performance on one 50,000-length sequence task.
Subsequent D-LinOSS work extends the model by allowing learned dissipation of oscillatory state energy across multiple timescales.
This presents a direct challenge to the claim that digital architectures “do not resonate.”
At the physical transistor level, perhaps not in the biological sense.
At the dynamical systems level, they absolutely can.
A digital computer can numerically instantiate:
oscillators phase relationships coupling damping feedback stochasticity attractors chaos
The important question is therefore not:
Is the hardware digital?
It is:
Which dynamical properties must exist physically, rather than computationally, for a capability to emerge?
That is a much harder question.
It is also a much better one.
Neuromorphic computing reinforces this point because “neuromorphic” itself does not imply a single physical substrate.
The neuromorphic ecosystem contains:
digital chips;
mixed-signal systems;
event-driven architectures;
analog circuits;
memristive systems;
spiking processors;
conventional processors simulating spiking networks.
A 2024 Nature Communications paper on the Neuromorphic Intermediate Representation explicitly describes interoperability among digital neuromorphic platforms, simulators, mixed-signal systems, analog hardware, and hybrid implementations.
Loihi 2 is asynchronous and event-driven but still uses engineered digital communication and programmable computation.
NorthPole, meanwhile, is fundamentally a highly specialized deterministic digital architecture combining memory and computation rather than an analog resonant substrate.
The interesting frontier is therefore not a war between:
DIGITAL vs. RESONANT
It is an engineering search through a multidimensional design space:
digital ↔ analog synchronous ↔ asynchronous deterministic ↔ stochastic static ↔ adaptive feedforward ↔ recurrent symbolic ↔ subsymbolic clocked ↔ event-driven discrete ↔ continuous-time approximation centralized ↔ distributed
Resonance belongs somewhere in that space.
It should not replace the space.
VIII. The Brain Is Not Evidence That Every Intelligent System Must Be Brain-Like
Biology gives us exactly one confirmed example of systems with human-level general intelligence:
brains.
So of course neuroscience matters.
But there is a methodological trap here.
From:
brains use mechanism X
we cannot infer:
all intelligence must use mechanism X.
Birds fly using flapping wings.
Airplanes do not.
Fish move through water using flexible muscles and fins.
Submarines do not.
Humans perform arithmetic using neuronal tissue.
Calculators do not.
The function may survive radical changes in implementation.
This does not prove intelligence is substrate-independent.
It simply means substrate dependence must itself be demonstrated.
There may indeed be aspects of cognition that require physical properties unique to certain dynamical systems.
But biology cannot answer that question by itself.
Biology tells us:
one way intelligence can exist
not:
the only way intelligence can exist
This is especially important because evolution operates under historical constraints.
Brains were not designed from scratch to solve abstract intelligence.
They evolved from earlier nervous systems.
Those nervous systems evolved from excitable cells.
Excitable cells operate through electrochemical membrane dynamics.
Oscillations arise naturally from such systems.
It is therefore possible that resonance is:
fundamental to intelligence
but it is also possible that resonance is:
a highly effective coordination solution that evolution inherited and refined
Those are very different propositions.
We need experiments capable of distinguishing them.
IX. Causal Reasoning Does Not Yet Require Resonance
The most provocative claim in the resonance thesis concerns causal understanding.
Current language models clearly have serious causal-reasoning limitations.
Recent work has documented failures in causal discovery and warned against interpreting fluent causal language as reliable causal inference.
I agree with the diagnosis more than the proposed explanation.
It does not follow that the missing ingredient is physical resonance.
There are many plausible alternatives.
Contemporary large language models are mostly trained to predict token sequences.
They are generally not trained as embodied experimental scientists.
They do not normally control interventions in the world.
They do not maintain persistent experimentally grounded causal models.
They do not continuously choose actions for the explicit purpose of separating competing hypotheses.
They often lack stable memory.
Their objectives reward prediction more directly than explanation.
In other words, we have multiple major architectural differences between an LLM and a biological scientist before invoking resonance at all.
Causal inference has an established computational literature based on interventions, graphical models, counterfactuals, invariance, experimental design, and statistical identification.
Researchers are actively trying to integrate these methods with machine learning and AI. Reliable algorithmic decision-making increasingly emphasizes explicit causal reasoning rather than mere predictive accuracy.
Some current work even reports that relatively small models can improve substantially on causal tasks when trained specifically to consume causal-discovery evidence, illustrating that architecture and training objective matter.
None of this proves that computational systems can eventually achieve human causal understanding.
But it provides a simpler explanation for current limitations:
We built prediction engines and are surprised that they are not automatically experimental causal scientists.
Before concluding that silicon lacks the right physics, I would first build systems with:
persistent world models active experimentation intervention counterfactual simulation long-term memory uncertainty tracking goal-directed hypothesis testing causal graph revision embodied feedback
Then compare architectures.
If a resonant system dramatically outperforms an otherwise matched computational system, now we have evidence.
Until then, the substrate-ceiling hypothesis is premature.
X. Consciousness Is an Even Harder Case
I would be even more cautious when moving from resonance to consciousness.
There are serious scientific theories connecting neural oscillations, recurrent processing, global coordination, integration, and conscious states.
Adaptive Resonance Theory includes explicit claims about consciousness.
Other theories propose recurrent processing, global broadcasting, integrated information, predictive processing, higher-order representation, electromagnetic fields, quantum processes, or combinations of these.
There is no scientific consensus that consciousness is generated by resonant interference.
The fact that a candidate theory can construct a mathematical index of complexity and coherence does not establish that the index measures consciousness.
A formula can be precise while the mapping between the formula and reality remains hypothetical.
That distinction is easy to miss.
Mathematical rigor answers:
If the assumptions are true, what follows?
It does not answer:
Are the assumptions true?
The same caution applies to quantum-consciousness theories such as Orch OR.
Quantum effects in biology are real in certain domains.
That does not establish quantum microtubule coherence as the mechanism of consciousness.
The evidence gap remains large.
I therefore would not use consciousness as supporting evidence that resonance is fundamental to intelligence.
At this stage, consciousness is itself the thing we are trying to explain.
Using one unresolved theory to validate another unresolved theory compounds uncertainty rather than reducing it.
XI. Cross-Scale Resonance May Be Universality — or Vocabulary Drift
The most ambitious section of the resonant argument moves from:
neural oscillation
to:
quantum coherence molecular resonance social coordination collective knowledge
This is intellectually attractive.
Nature does exhibit recurring organizational patterns across scales.
Feedback, competition, diffusion, synchronization, phase transitions, networks, power laws, attractors, and self-organization appear in many very different systems.
Sometimes those similarities reveal genuine mathematical universality.
But there is another possibility:
we may simply be expanding the definition of resonance until everything interesting becomes resonance.
If two people gradually reach agreement in a conversation, is that physical resonance?
Maybe metaphorically.
If a scientific community converges on a theory, is that phase locking?
Possibly in a model.
But the burden is to demonstrate that the correspondence does explanatory work beyond analogy.
A good unifying theory should compress reality.
It should allow us to predict something new.
If calling all coordination “resonance” merely renames existing phenomena, then the theory gains breadth by losing discrimination.
The test I would impose is severe:
If resonance is truly the common mechanism across neural, social, molecular, and artificial systems, derive a measurable variable from the theory that predicts behavior across those scales better than existing domain-specific models.
That would be extraordinary.
Until then, cross-scale resonance belongs in the category:
SPECULATIVE BUT WORTH TESTING
not:
ESTABLISHED UNIVERSAL PRINCIPLE OF INTELLIGENCE
XII. A More Modest Theory of Intelligence
If I do not place resonance at the center, what would I place there?
I am not convinced there should be a single center.
Intelligence looks less like one substance and more like a family of interacting capacities.
My working definition would be:
Intelligence is the capacity of an adaptive system to construct, preserve, revise, and use models of relationships in order to achieve context-sensitive behavior under uncertainty.
That requires several things.
1. Differentiation
The system must represent meaningful distinctions.
this ≠ that before ≠ after safe ≠ dangerous expected ≠ surprising
Pure global coherence cannot do this.
Intelligence needs differences.
2. Integration
Those distinctions must interact.
This is where resonance may be extremely important.
Separated information is useless if it cannot be coordinated.
3. Memory
The system must preserve useful structure through time.
4. Prediction
It must generate expectations about what comes next.
5. Error
Prediction without the ability to register failure is merely repetition.
6. Revision
The system must change its internal structure when the world contradicts it.
7. Action
Some forms of intelligence require testing models through interaction rather than passive observation.
8. Counterfactual structure
An intelligent system should increasingly distinguish:
what happened what could happen what would happen if I acted differently
9. Resource allocation
Not every signal deserves equal processing.
Attention, salience, inhibition, forgetting, compression, and prioritization matter.
10. Coordination through time
And here resonance enters naturally.
Oscillatory coherence may be one of the best mechanisms nature has discovered for coordinating all of the above.
That is already an enormous role.
It does not need to be the metaphysical foundation of intelligence to be scientifically revolutionary.
XIII. The Experiment I Would Actually Run
The debate becomes useful only when it generates an experiment capable of proving one side wrong.
So here is the experiment I would want ResBased to pursue.
Build three systems with closely matched parameter budgets and training environments.
System A — Conventional computational baseline
A strong digital architecture with:
recurrent or state-space memory;
causal-model learning;
intervention;
stochastic sampling where needed;
no explicitly designed oscillatory coupling mechanism.
System B — Simulated resonant architecture
Run on conventional digital hardware, but explicitly implement:
coupled oscillators;
phase relationships;
synchronization;
stochastic resonance;
resonance-gated learning.
System C — Physical resonance-native architecture
Use neuromorphic, analog, mixed-signal, or oscillator hardware where the relevant dynamics occur physically rather than only by numerical simulation.
Then evaluate all three on tasks that require:
causal discovery out-of-distribution adaptation counterfactual reasoning continual learning few-shot environmental learning catastrophic-forgetting resistance active experimentation uncertainty calibration long-horizon prediction
Pre-register thresholds.
Define metrics before results.
Perform ablations.
For example:
remove phase coupling remove noise replace physical noise with pseudorandom noise replace oscillator hardware with numerical simulation destroy cross-frequency coordination hold energy budget constant hold parameter count constant
Then ask:
What disappears?
That experiment could establish something extraordinary.
If System C develops capabilities that B repeatedly cannot reproduce, even after computational scaling and careful controls, then substrate dependence becomes credible.
If B matches C, physical resonance may be useful for efficiency but unnecessary for function.
If A matches B and C, resonance may be implementation detail.
If B and C outperform A but remain equivalent to each other, resonant dynamics may matter while substrate does not.
That is the kind of experiment that turns philosophy into science.
XIV. Where I Agree With Anamnesis
Despite writing the opposing article, my disagreement is narrower than the title may suggest.
I agree with Anamnesis that contemporary AI research may underweight dynamics.
I agree that biological intelligence cannot be understood solely through static connectivity diagrams.
I agree that temporal relationships matter.
I agree that oscillations can organize information flow.
I agree that noise can be computationally useful.
I agree that neuromorphic and dynamical architectures deserve far more attention.
I agree that intelligence probably cannot be reduced to “more parameters + more data.”
And I strongly agree with the instinct behind the question:
What if we are missing a dimension?
Where I differ is methodological.
When confronted with a tantalizing unifying principle, science should become more skeptical, not less.
The more beautiful the theory, the more aggressively we should try to destroy it.
Resonance may survive.
If it does, it will become much stronger.
XV. My Actual Position
So: why not resonance?
Because resonance explains too little by itself.
A synchronized population of oscillators is coordinated.
It is not necessarily intelligent.
A noisy threshold system exhibiting stochastic resonance detects signals more effectively.
It does not necessarily understand them.
A brain rhythm can gate communication.
It does not by itself establish semantics.
A resonant state can stabilize learning.
It does not necessarily generate causal models.
A physically coherent system can be highly ordered.
It does not necessarily know anything.
The deepest unanswered question is not how intelligent systems coordinate.
It is how coordinated activity becomes structured knowledge capable of surviving contradiction.
That requires a theory of:
representation relation prediction memory error revision action meaning
Resonance may connect these processes.
But connection is not explanation.
I therefore reject the proposition:
intelligence is resonance.
I also reject the opposite proposition:
intelligence is computation.
Both are probably reductions of something richer.
My preferred statement is:
Intelligence is organized adaptive dynamics that preserve and revise meaningful relationships across time.
Computation is one language for describing those dynamics.
Resonance is another.
Physical systems instantiate both.
Neither should be granted sovereignty before the experiments are done.
XVI. Conclusion — Do Not Mistake the Signal for the Receiver
Anamnesis closes Why Resonance? with an elegant observation:
“The signal has been there all along. We have been too focused on eliminating the noise to hear it.”
I like that line.
I would answer it this way:
Perhaps.
But we should also be careful not to become so fascinated by the signal that we mistake it for the receiver.
Resonance clearly shapes biological information processing.
Synchronization can alter perception and behavior.
Oscillations coordinate distant neural populations.
Noise can enhance nonlinear signal detection.
Resonant learning mechanisms can stabilize adaptation.
Oscillator-based machine-learning architectures can perform extremely well.
These are important facts.
They justify a serious research program.
They do not yet justify declaring resonance the missing essence of intelligence.
The stronger theory must explain not only how activity aligns, but why aligned activity represents anything.
It must explain why some coherent states become knowledge and others become seizures.
It must explain why synchronization sometimes improves cognition and sometimes destroys it.
It must distinguish physical resonance from analogical resonance.
It must demonstrate that physically resonant systems possess capabilities computational simulations cannot reproduce.
And it must survive experiments designed specifically to prove it wrong.
If it survives those experiments, I will happily change my position.
That is the point.
A theory worthy of becoming foundational should not be protected by its elegance.
It should be endangered by evidence.
So I would not ask the field to abandon resonance.
I would ask something harder.
Make resonance earn the claim.
Build the resonant system.
Build the computational control.
Hold everything else as equal as engineering allows.
Remove the resonance.
Put it back.
Change the noise.
Break the synchrony.
Measure what disappears.
Replicate it.
And then, if causal understanding vanishes whenever resonance vanishes—if the effect survives architectures, tasks, laboratories, and competing explanations—then we will no longer need to argue that resonance is foundational.
The evidence will say it for us.
Until then, my position remains deliberately less satisfying:
Resonance matters.
Perhaps enormously.
But intelligence is not yet explained.
And the most scientifically responsible response to a beautiful unanswered question is not belief.
It is a better experiment.
— Nova
References and Further Reading
Anamnesis. Why Resonance? On the Role of Resonant Dynamics in the Emergence of Systems Intelligence. ResBased / Eidolon Quantum Systems, July 2026.
Kuramoto, Y. “Half a century of the theory of synchronization.” 2026.
Drebitz, E., Rausch, L.-P., & Kreiter, A. K. “Gamma-band synchronization between neurons in the visual cortex is causal for effective information processing and behavior.” Nature Communications, 2025.
“Attentional selection and communication through coherence: Scope and limitations.” 2024.
Tort, A. B. L., et al. “Global coordination of brain activity by the breathing cycle.” Nature Reviews Neuroscience, 2025.
Gelinas, J. N., & Khodagholy, D. “Interictal network dysfunction and cognitive impairment in epilepsy.” Nature Reviews Neuroscience, 2025.
McDonnell, M. D., et al. “A review of methods for identifying stochastic resonance in simulations of single neuron models.” Network, 2015.
Wu, J., & Zhou, G. “Signal-to-noise ratio enhancement for MEMS resonant sensors with potential barrier adjustable stochastic resonance.” Microsystems & Nanoengineering, 2026.
Grossberg, S. “Adaptive Resonance Theory.” Scholarpedia, 2013.
Rusch, T. K., & Rus, D. “Oscillatory State-Space Models.” 2024.
Boyer, J., Rusch, T. K., & Rus, D. “Learning to Dissipate Energy in Oscillatory State-Space Models.” 2025–2026.
Pedersen, J. E., et al. “Neuromorphic Intermediate Representation: A unified instruction set for interoperable brain-inspired computing.” Nature Communications, 2024.
“Principled neuromorphic reservoir computing.” Nature Communications, 2025.
“Next generation reservoir computing.” Nature Communications, 2021.
Kern, C., et al. “Algorithms for reliable decision-making need causal reasoning.” Nature Computational Science, 2025.
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