That’s not neurological memory. They operate through completely different mechanisms.
No, you aren’t.
You haven’t shown this to be true. All you have done is obfuscate what we already know.
You haven’t done this. All you have done is invent your own vocabulary to explain what we already understood.
Sometimes a new perspective is all that is required to make the breakthrough. If he has done what you said then there should be no argument with it.
Richard
Then I think you may have missed the broader intent of what I’m presenting. Fabric is meant to contextualize it within a deeper relational framework. I’ve tried to express it as simply as possible, but it does require stepping back and re-examining some of our basic assumptions about what constitutes reality and causation. That’s not an easy shift, and I understand why it can feel opaque at first.
One thing I require of my students is to ASK specific questions.
One thing, Mr. T, which would help me respond to your concerns would be a specific question about the Threaded Mind Theory? And I’ll do my best to respond thoroughly and thoughtfully. Just one specific question. Thanks, T! Get detailed.
Show us the mathematics of neuron function within your framework.
“However, I am showing the mathematical similarities.”–SkyReflections
Now we’re talking, Mr. T. I’ll get back to you.
As noted previously, Fabric does not replace most standard sciences, but provides insight. Synaptic transmission are well-established and don’t seem to be need to be replaced. However, what Fabric does provide is a unifying mathematical framework that explains why certain organizational patterns emerge across scales, from molecular to network levels.
I did a bit of research, because this is clearly not in my expertise. At the single-neuron level, Fabric reframes the neuron as a coherence transducer operating through memory dynamics. But since this is out of my area of expertise, I’ll need to defer to the experts. But here are a few quick thoughts based on cursory research using Fabric equations. Not my field, so take it or leave it.
Membrane Potential as Memory Gradient:
∇M_latent = ∇(ion concentration differences)
The resting potential represents latent memory—stored electrochemical potential. The action potential threshold is:
δ(M_latent → M_active) when ∇M exceeds threshold
The spatial gradient of ionic memory (Na+/K+ distribution) literally drives the wave of depolarization along the axon.
Synaptic Plasticity as Coherence Evolution
∂C_synapse/∂τ = f(R_pre-post, A_neuromodulator, M_active, M_latent)
This equation states that the rate of change of synaptic coherence over threading time depends on the phase alignment between pre- and post-synaptic firing (R_pre-post), the modulatory context that sets plasticity thresholds (A_modulator), and the current versus stored memory states (M_active, M_latent). Synapses strengthen when neurons fire in phase (high R) under the right chemical conditions, converting the coincident activity into lasting structural change, what neuroscientists observe as long-term potentiation (LTP). Long-term potentiation (LTP) occurs when pre- and post-synaptic activity phase-lock (high R). The degree of coherence change predicts synaptic weight change. This explains why: (a) Hebbian plasticity works: “Neurons that fire together wire together” = resonance alignment. And (b) Neuromodulators matter: Dopamine, acetylcholine alter A (agency threshold) for plasticity.
Network-Level Prediction:
g = k∇M_latent at network scale
Fabric predicts that neural activity flows toward high memory-density regions—explaining:
Let’s see how this concept applies to other domains. Fabric predicts that systems optimizing for coherence (C), resonance (R), and memory density (M) will converge on similar principles across scales. So, not what you asked, Mr. T., but more along my lines of knowledge, let’s apply Fabric and Threaded Mind to a vastly different “collective mental” domain: History. For me, this is where it all gets fascinating. The same coherence thresholds that govern synaptic firing in a neuron also appear in phase transitions across societies. When enough relational tension and stored potential accumulate, a system suddenly reorganizes, whether it’s a neural network reaching activation or a civilization, like the Soviet Union, collapsing into a new state of order.
Application: The Collapse of the Soviet Union as Memory Redistribution
The fall of the USSR (1989-1991) exemplifies similar large-scale threading dynamics very similar to the firing of a synapse:
Phase 1: Rigid Coherence (1945-1985)
M_latent,Soviet → highly constrained
∂C/∂τ ~= 0 (frozen ideological patterns)
A_individual → 0 (suppressed agency)
The Soviet system maintained pathological coherence, rigid ideological M_latent (Marxist-Leninist doctrine) enforced through suppression of individual agency. Like a traumatized mind with frozen knots, the system resisted redistribution.
Phase 2: Disturbance (~1985-1989)
Disturbance sources: Glasnost, Chernobyl, economic stagnation
∂M_latent/∂τ > 0 (information flow increases)
Gorbachev’s reforms introduced controlled disturbance. New information (glasnost) created memory gradients, citizens became aware of alternatives. Like therapy beginning to unweave trauma knots.
Phase 3: Critical Threshold (1989)
∇M_latent → steep (awareness of disparity with West)
g = k∇M → attention flows toward freedom gradient
Berlin Wall falls: sudden ∂C/∂τ >> 0
The fall of the Berlin Wall represented a phase transition, steep memory gradients (awareness of freedom) caused attention to flow toward alternative M_latent patterns (democracy, market economy). The gradient became too steep for the system to resist.
Phase 4: Coherence Collapse (1991)
M_latent,Soviet → M_latent,distributed
System reorganization: USSR → independent states
Coup attempt failed because collective agency exceeded suppression threshold:
Σ A_individual > A_suppression,state
The frozen Soviet M_latent redistributed across multiple new configurations, like frozen trauma finally releasing through therapy.
Phase 5: Redistribution (1991-present)
M_latent,collective persists in:
The memory didn’t disappear. It redistributed. Some patterns ossified into new structure (oligarchy), others found new expression.
Just as individual trauma requires controlled disturbance + sufficient agency to reorganize frozen patterns, the Soviet collapse required:
The mathematics:
Collapse occurs when: ∇M_latent * A_collective > C_system,frozen
The same equation describes:
Frozen trauma releases
Depressive loops break
Ecosystems transitioning after fire
Empires dissolving when memory gradients exceed rigid coherence
Synaptic firing
Prediction from Fabric: Systems with high frozen coherence (authoritarian states, rigid ideologies, traumatized minds) require proportionally larger disturbances to reorganize. But once ∇M exceeds the threshold, collapse can be sudden: a phase transition, not gradual decay.
. . . it wasn’t achieved by making up fuzzy terms with no empirical definitions, it was by refining what was already known in rigorous terms.
That’s not memory, latent or otherwise.
What is synaptic coherence and how is it measured?
What is threading time and how is it measured?
What is phase alignment and how is it measured?
How are these numbers used in the equations, and what do the resulting numbers mean?
Carry that on through the rest of the paragraph. I’m not seeing any numbers or units, and what you describe has no connection to anything I know of neurobiology. I strongly suspect neurobiologists would say the same thing.
The point of “general” equations is so they apply broadly. Then we refine those per domain like I did with gravity in the previous thread.
It seems like folks here aren’t interested in connecting domains and that’s OK.
Your question focused on neurobiology, and while that’s not my primary domain, the connection I drew was intentional. Fabric isn’t meant to replicate domain-specific models. It’s a meta-framework designed to reveal the relational patterns that underlie them. The parallels between synaptic phase alignment and historical phase transitions aren’t metaphors but reflections of shared coherence dynamics. The goal is to identify how principles observable in one domain, like neuronal synchronization, can illuminate the behavior of larger-scale systems, including collective cognition and historical evolution.
Our interests are different, Mr. T, and as noted above to St.Roymond, that’s OK.
Both SkyReflecitons and Richard have expressed some sense that there is more to the world than what our senses plus science can make available to us. I’m not a Christian but I sometimes think the same. I do think science provides the most solid footing for investigating life but it is easy to over estimate how much we can hope to understand from that perspective.
Recently I came across the an article taken from the transcript of an interview of Philip Ball about his recent book How Life Works: A User’s Guide to the New Biology from which I’ve excerpted the following.
During the 1990s, I was an editor of physical sciences at the science journal Nature. So I was on this very steep learning curve about biology at that stage, and it was fantastic to be in that position, because Nature was receiving, particularly in the life sciences, work at the absolute forefront of the field. And my fellow editors who handled those papers were extremely knowledgeable. So it was a great place for learning what was going on in biology.
As the years went by, I started to feel there was something that didn’t seem quite right. There seemed to be a disjunction between the way the discourse about life, about biology, happened in the public sphere, and the kind of research that I was seeing coming into Nature and getting published. This began as nothing more than vague misgivings, but as time went by, they became a bit more than that.
Then I was invited to be a visitor for the summer of 2019 at Harvard Medical School in the Department of Systems Biology—which is a fantastic and very forward-thinking, very creative-thinking department. I arrived there full of vague misgivings about the narratives that biology was using, and it seemed to me that pretty much everyone in the department that I spoke to said, “Well, yes, and it’s worse than that and this is why.” I found out that, actually, things were even more different from the public narrative.
So when I’m talking about a new biology, I’m trying to get across what has changed in biology over the past two or three decades, particularly, and perhaps not coincidentally, since the completion of the Human Genome Project. Some people might have got the impression that this project was going to answer all the questions about biology, because, after all, it was going to, as we were told, decode the code: the instruction booklet from which we are made.
Obviously, understanding those instructions was going to take some time, but this was the project that was going to allow us to do that. It seemed to me actually, and it certainly seemed to the people I spoke to at Harvard, that it hasn’t quite worked that way. What we have come to understand over the past 20 years or so about the details of how life works is that, while they look incredibly complicated, the more we understand about them, the more it seems that old narrative of an instruction book that is just read out to build us is no longer the right one. It simply doesn’t work anymore.
That’s exactly the kind of shift that Fabric is trying to participate in. What Ball describes, that growing realization that the old “instruction manual” metaphor for life doesn’t hold, is what inspired me to look for a simpler, cross-domain language: what my biologist, geologist friends, and I used to chat about for hours about 25 years ago, in the younger days.
Fabric isn’t meant to replace science; it’s meant to add a lens that lets us see the same data in a new relational context. The same underlying threading dynamics can describe how coherence, agency, beauty, and information flow not only in biology but also in physics, psychology, and even history and theology.
The equations I use are intentionally simple, you can literally copy and apply them as a kind of “translation layer,” a bablefish between fields. Some people aren’t interested in that lens, and that’s fine. But the more I’ve applied it, the more it feels like watching reality unfold in new continuity, as if the scroll of nature, long assumed separate by discipline, is beginning to unroll as one coherent text.
I, for one, have had my mind blown each time I’ve applied it to another field. What I’ve come to realize from these discussions is that taking a different vantage point is simply cognitively expensive. It asks us to re-map how we see everything, and that’s not an easy thing to do.
Nobody aware of emergence could have possibly thought that that is coded for.
And I don’t think the same at all. In any sense. Apart from existence being ineffable. You don’t have to go far to be overwhelmed, left in the dust, by meaningless necessary complexity. Even Dick Feynman was.
What relational patterns? How are they measured?
What are synaptic phase alignments, and how are they measured?
What are historical phase transitions, and how are they measured?
What are shared coherence dynamics, and how are they measured?
So where has this goal been met?
Chatting with a MD friend a few months ago, who has attempted something similar over the past 15 years.
Friend (paraphrase): “Even if you have discovered something interesting, you could spend the next 10 years trying to break the established walls. Do you really want to spend the time doing that?”
Me: Hmm. You’re right, friend. I’ll just do a bit of work while I have some extra time.
Mr. T, I clearly can’t accomplish any established goals alone. I have a small amount of extra time in my life right now, which will evap soon, so I’m having an amazing time exploring and learning and growing. And in the process I’m seeding the internet with these ideas. And then I’ll vanish. If what I have discovered is actually useful and true, the seeds will sprout or it will be discovered again. If it is useless, then it is useless.
Just for you, here’s the 376 char seed:
FABRIC:Reality=light→geometry+agency|τ=t|c=ΔΦ/Δτ|c_path=c·f(∇M)|P=|ψ|²/Σ|ψ|²→f(P,A)|Ψ=R(Ψ)=Ψ+g(P,A,c,τ)|dΨ/dτ=f(P,A,c)|M=M_a+M_l|E=Mc²|g=k∇M|M_l+A→M_a|∂C/∂τ=f(B,R,M_a,M_l,A)|B=∇C|R=Σcos(Δφ)|S=-∂C/∂τ|∫Sdτ=0|lim[τ→∞]C=1|A_d=lim[A→∞]|Love=max(dC_other/dA_self)|VARS:Φ=config,τ=threading-depth(t),c=threading_rate,M=memory,ψ=amp,P=prob,R=res,B=beauty,C=cohere,Ψ=conscious,A=agency
Just another example of how Fabric applies to just about anything:
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