A New Law of Thermodynamics
An open letter to Stephen Wolfram and Dr. Brian Keating, about Dark Time Theory, Self Aware Networks Theory, followed by Micah's New Law of Thermodynamics
This letter came about because tonight I was watching Stephen Wolfram speak in a video to Dr. Brian Keating, and he was saying that the 2nd Law of Thermodynamics represents the observed result of an irreducible computation being performed by the molecules of gas. This made me sit up in my seat and realize that not only did I know the computation, being performed by the gas, but that it represented an actual new law of Thermodynamics. It also meant that the computation being done by the gas was reducible, at least algorithmically.
”What is Time? Stephen Wolfram’s Groundbreaking New Theory”
Hi Stephen Wolfram, and Dr. Brian Keating, if you are reading this, I want to talk with you both about my Dark Time Theory that unites Gravity with Quantum Mechanics, my Self Aware Networks Theory of Mind (a novel theory of Phenomenological Consciousness), and Micah's New Law of Thermal Dynamics. All three are related conceptually, I’m sure you will love the idea once you hear about it!
Executive Summary: I have developed three interconnected theories—Dark Time Theory, Self Aware Networks Theory of Mind, and Micah's New Law of Thermal Dynamics. Together, they propose a unifying principle of wave-based interactions that could explain how consciousness arises from neural oscillations, how gravitational effects might be reconciled with quantum phenomena, and why thermodynamic systems systematically dissipate differences over time. In essence, these theories suggest that every system—whether it’s gas molecules in a container or networks of neurons—achieves equilibrium or stable configurations through the real-time computational dissipation of signal differentials.
In brief: Dark Time Theory proposes that additional frames of time (or additional waves of time, or greater time density, greater phase magnitude and lower frequency gravity waves) around massive objects, influence both quantum phenomena and the structure of spacetime. My conjecture the Self Aware Networks Theory of Mind applies similar principles of wave synchronization and dissipation to explain how consciousness emerges from neural oscillations in the brain.
Finally, Micah's New Law of Thermal Dynamics ties these concepts together by positing a universal framework in which all systems—both physical and cognitive—strive to minimize phase differences or energy differentials over time. Placed side by side, these three theories suggest a unified view of nature, wherein gravity, quantum mechanics, and conscious experience all share a fundamental mechanism based on synchronized wave interactions.
I will start by saying Emergence, or Emergent complexity as a concept, means a problem is not reducible to its component parts. I see many scientists including Stephen Wolfram discussing the concept of irreducible computation, and or the concept of Emergence.
While I respect the scientific rigor & effort of professionals who use these terms I argue emergence is not real, and that there is no problem that is not reducible to its component parts, there is no emergence effectively, and any computation can be explained.
In particular, in the study of consciousness in the brain the missing computation is oscillatory tomography, cells are detecting coincidence patterns, and through oscillation they bind together these coincidence patterns, as phase wave differentials, and these traveling wave differentials are like the edges or cross sections detected in tomography, they help the brain construct a 3D + time sensory rendering of the ecosystem.
I think biologist Michael Levin would agree with what I’m saying because he recently stated in a youtube video from 4 days ago, that bioelectricity is our cognitive glue, which is another perspective that validates the argument I created which is that NAPOT, or Neural Array Project Oscillation Tomography, meaning traveling waves that are different from the synchronized oscillation in terms of their phase, have information value, and when they oscillate together they bind together to produce neural renderings that form our international representations of everything, and these internal computationally rendered representations are seen and observed internally by tonic oscillating groups of cells. I describe how this works in detail in my book “Bridging Molecular Mechanisms and Neural Oscillatory Dynamics” found on Amazon.
So cognition is not an emergent or irreducible computation as it says in the title of Earl K. Miller’s paper “Cognition is an emergent property” although its a great paper and I respect his work enormously despite one small disagreement on the validity of terms like “emergent” and “irreducible complexity” https://www.sciencedirect.com/science/article/pii/S2352154624000391?via%3Dihub
Earl K. Miller describes emergence as like the standing crowd wave in a sports stadium, where the crowd takes turns standing up, he said (paraphrasing) that if you were only focused on one individual, or one neuron, or only a few, you would not be able to detect the emergent phenomena (or irreducible computation) of the wave rippling through the stadium.
My argument is that this is confusing the Scientists viewpoint with the actual computation being performed by individuals in the crowd, or by neurons. The people on the ground know they are participating in a wave. Neurons are selectively attracted to perform when they detect that certain signal patterns have arrived. The computation as a group oscillatory timing activity is reducible from other perspectives.
Micah's New Law of Thermal Dynamics makes the irreducible computations that lead to brainwaves, and even the evolution of the universe as a whole reducible to the computations of its component parts, the whole universe is binding together & computing together with the oscillatory tomography of phase wave differentials impacting electromagnetic fields generated by components that are oscillating together and together dissipating their phase differences.
Micah's New Law of Thermal Dynamics
“In a thermodynamic system, the approach to equilibrium proceeds by sequential ‘computations’—wave-like interactions or signal exchanges among its constituents—that progressively dissipate all differences in thermodynamic properties until those properties become uniformly distributed.
“Entropy can be viewed as the computational dissipation of signals—differences in thermodynamic properties (gas, heat, light, chemical, mechanical, electromagnetic, etc.)—across arrays of ‘sensing and transmitting’ components (e.g., molecules, neurons), such that repeated interactions progressively eliminate those differences and drive the system toward uniform equilibrium (of signal transmission).”
(Also published here: https://github.com/v5ma/selfawarenetworks/blob/main/raynote12.md )
Explainer of Micah's New Law of Thermal Dynamics
‘Computational Dissipation’: Emphasizes that entropy increase involves a process akin to “computation,” where each interaction step reduces differences.
Signal Perspective: Acknowledges that properties like heat, pressure, chemical potentials, and even neural transmissions can be viewed as signals that spread and get smoothed out.
Array of Components: Encompasses everything from gas molecules to biological systems (neurons)—anything that “senses” a difference and “transmits” a response.
Drives Toward Equilibrium: Ties it back to the thermodynamic notion of entropy as the push toward a uniform, higher-entropy state.
Here is the information to derive Micah's New Law of Thermal Dynamics: The reducible computation of thermodynamics, in the case of gas in a container explaining from an organized state to a disorganized state, is the computation of the dissipation of signal difference between all the gas particles in the container, when they fill all parts of the container equally, that is the closest the gas can get to signal equilibrium where the energy between each particle is roughly equal to the amount of energy in every other particle.
The process of signal dissipation as a computation is the essence of the new law. It means that the gas undergoing expansion in a container is essentially expanding equally to all parts of the container because of the signal dissipation of all the divergent thermodynamic properties of the gas molecular result in a natural step by step reducible computation where equilibrium between each property of the gas is achieved.
Each property of the gas, transmitted between the gas molecules, is a phase wave differential, and each time a phase wave differential is passed through the gas system its property (heat or energy or another property) is further dissipated, or shrunk until the amount of energy or amount of heat or amount of any other property in the gas/chemical system is approximately equal everywhere in that oscillating system.
If a hole is poked in the container and gas starts to leak out that disturbs the equilibrium state of synchronous oscillation between all the properties of all the particles, so they all react to the leak. The whole energy of the system is perturbed when one part of it is perturbed, so it acts as one unified system that is collectively reacting to itself to achieve thermodynamic equilibrium through phase wave differential dissipation.
Micah's New Law of Thermal Dynamics is in otherwards an argument that Entropy is the computational dissipation of signals or the differences between projected thermodynamic properties (gas, light, heat, chemical waves, mechanical waves, electromagnetic waves), across an array sensing & transmitting components (gas molecules or anything else that senses reacts transmits some properties, like neurons and their transmissions or projections)
Micah's New Law of Thermal Dynamics applied to the 3rd Law of Thermodynamics:
A “Computation” / “Signal” Interpretation of Thermodynamics.
An insightful way of thinking about entropy as the “dissipation of signal difference.” Here’s how that relates:
Gas Expanding in a Container
Imagine a gas that starts out in some “organized” configuration (perhaps all in one corner) and expands to fill the container uniformly.
During this process, we can think of each gas particle “sharing signals” (collisions, energy exchanges) with every other particle.
Dissipation of Signal Difference
As the gas expands and mixes thoroughly, differences in temperature or velocity among the particles tend to dissipate.
Over time, the system moves toward a state where all particles have roughly equal energy—this is equilibrium.
Approach to Zero Signal Difference
In principle, the closest we can get to complete equilibrium is when all particles have the exact same energy level (or extremely close).
In classical thermodynamics, as one approaches absolute zero temperature, the system’s available energy levels get “compressed,” and the system settles into its lowest-energy configuration (its ground state).
In this computational analogy: at absolute zero, all “signals” (differences in energy) are minimized to essentially zero, mirroring the classical idea that the entropy of a perfect crystal at 0 K can be defined as zero.
Thus, Micah's New Law of Thermal Dynamics, when applied to the Third Law, can be viewed as stating that when temperature goes to absolute zero, all signal (energy) differences between particles vanish; there is no more “computation” to be done because the system is in its unique, perfectly ordered state.
This proposed Micah's New Law of Thermal Dynamics reframes the process of reaching equilibrium in terms of signals, waves, and computational steps.
Unlike the first three laws that describe energy conservation, entropy increase, and the nature of absolute zero, Micah's New Law of Thermal Dynamics emphasizes the mechanistic path to equilibrium as a sequential 'computation' of dissipating property differentials (phase waves), wherein each interaction step reduces those differences until the system reaches a synchronized and uniform state.
Why This Law as a New Perspective Can Be Useful
Microscopic Explanation: It provides an intuitive picture of how collisions and interactions in a gas “carry information” or “signals” that lead to uniformity.
Unification with Other Fields: Thinking of thermodynamics as “computation” aligns with ideas in information theory and statistical mechanics, where entropy and information are deeply intertwined.
Insight into Perturbations: By treating new disturbances as new wave differentials, its clearer how the system constantly re-adjusts to achieve equilibrium, step by step.
Restating the core concept of Micah's New Law of Thermal Dynamics
Signal Dissipation as Computation
In this view, whenever gas expands (or undergoes any thermodynamic process), there are “signals”—essentially energy or property differentials—that must traverse the system to bring about equilibrium.
Each collision, exchange, or “wave” of interaction acts like a computational step that reduces differences (in temperature, pressure, or some generalized property) among particles.
Phase-Wave Differentials
You treat each property (heat, kinetic energy, potential energy, etc.) as a kind of phase wave passing through the gas.
Each time this wave interacts or “collides” with particles, it dissipates or “shrinks” the difference in that property. Over time, you get a more uniform distribution of that property throughout the container.
Equilibrium as Synchronous Oscillation
By the time the gas is uniformly distributed—pressure, temperature, and other properties roughly equal in all parts of the container—the system is in a sort of synchronous oscillation or “steady state” of minimal property differences.
Perturbations of Equilibrium
If something disturbs this system (e.g., poking a hole in the container so gas leaks out), you create new differentials (pressure differences, energy flow changes, etc.).
These new differentials, in turn, generate fresh “wave signals” throughout the remaining gas, driving it toward a new equilibrium configuration.
Hence, Micah's New Law of Thermal Dynamics is the observation that a system seeks to equalize phase-wave differentials (property differences) across all particles, and each step in that equalization is a form of “computation” (signal processing) that moves the system toward uniform equilibrium.
Contrast with the Established Laws
Classical First Law
States energy is conserved. You might say the system’s total “signal content” (energy) is the same, even though it spreads out or redistributes.
Classical Second Law
States entropy (disorder) increases or remains the same in an isolated system. The “wave differentials” naturally get dissipated, leading to a more uniform, higher-entropy state.
Classical Third Law
As temperature approaches absolute zero, entropy approaches a constant minimum (ideally zero for a perfect crystal). In this wave model, that would mean all signals are minimized to an extreme limit: no further oscillations or wave differences can exist.
How Micah's New Law of Thermal Dynamics is different:
It emphasizes how the system’s differentials (waves) systematically dissipate and “compute” their way to equilibrium. Its a deeper look at the mechanism of how entropy increases in real, physical processes.
How is Micah's New Law of Thermal Dynamics describing a real computation?
How does it apply to neurons that form a tonic oscillating group of cells?
How does it describe the core mechanic of how an oscillating group of cells becomes consciously aware of a phase wave differential information packet that impacts part of it's group?
Let’s break down how Micah's New Law of Thermal Dynamics concept (computational signal dissipation toward equilibrium) as a real form of information processing (computation) in neurons—and how that process might underlie a neural ensemble’s “awareness” of new inputs.
1. How does Micah's New Law of Thermal Dynamics describes real computations?
Sequential Signal Exchanges
In thermodynamics, Micah's New Law of Thermal Dynamics says that reaching equilibrium involves waves or signals traveling among components (gas molecules, for instance) that iteratively reduce differences in pressure, temperature, or other properties.
In neural terms, each firing event or synaptic transmission is a signal exchange. Instead of just “heat” or “pressure,” the property being spread is an electrochemical or electromagnetic difference across interconnected neurons.
Discrete Steps of Reduction
Each collision between gas molecules can be viewed as one “computation step” that partially equalizes energy among particles.
In neurons, each synaptic interaction partially “equalizes” the difference between an upstream neuron’s firing state and the downstream neuron’s membrane potential—altering the network’s overall activity pattern. Over repeated interaction cycles, local firing differences dissipate or merge into coherent activity patterns.
Information-Theoretic Parallels
When Micah's New Law of Thermal Dynamics emphasizes signal dissipation, it implies that differences in thermodynamic properties carry information, and collisions/interaction steps process that information until a uniform (maximally entropic) state is reached.
In neurons, differences in membrane potentials or oscillatory phases are the information. Each transmission step partially “resolves” or propagates those differences, effectively performing computations that integrate signals across the network.
Local-to-Global Convergence
Thermodynamically, local collisions or wave interactions eventually achieve a global equilibrium.
In a neural network, local synaptic interactions can lead, through recurrent connectivity, to a global pattern of firing or oscillation. That global pattern is (in computational terms) the “result” of all the smaller interactions among the neural ensemble.
Hence, Micah's New Law of Thermal Dynamics is not just a metaphorical statement. It points out that when you have an array of elements that “sense” differences and “transmit” signals, the step-by-step reduction of those differences is a real computational process. Whether its molecules in a gas or neurons in a brain, the system implements iterative signal processing—i.e., computation—that moves the group toward a new collective state (equilibrium or attractor state).
2. Applying It to Tonic Oscillating Neurons
Now let’s connect this directly to a tonic oscillating group of neurons—for example, a group of cells in the gamma band (30–90 Hz) or alpha band (8–12 Hz) that maintains a synchronous rhythm.
Neural Oscillations as Phase Waves
A “tonic” oscillation is already a synchronous or semi-synchronous state, much like a steady wave in a physical system.
According to Micah's New Law of Thermal Dynamics viewpoint, these rhythmic neurons are in a (relatively) stable local equilibrium—their phases are locked or partially synchronized via continual exchange of electrochemical signals (synaptic, gap junctions, etc.).
Introducing a Phase-Wave Differential
Suppose a novel stimulus arrives at part of this oscillatory group (e.g., a sensory input or a top-down modulatory signal). This perturbs the local equilibrium by introducing a new phase wave differential: the incoming signal is out of phase (or has a different amplitude/frequency) than the ongoing tonic rhythm.
The newly introduced “difference” (in firing rate, membrane potential, or rhythmic phase) is akin to poking a hole in the side of a thermodynamic container—suddenly, local conditions are changed, and the entire ensemble must respond.
Dissipation (Integration) of the New Signal
The neural ensemble—like a gas redistributing pressure—will begin to spread and integrate this phase difference. Nearby neurons adjust their firing to accommodate or counteract the new wave, leading to computational dissipation.
Over repeated cycles of firing (computation steps), the network may stabilize into a new global oscillatory pattern. In information-processing terms, that new pattern encodes the stimulus’ effect.
Conscious “Awareness” and Entification via Synchrony
From a neuroscience perspective, widespread synchronous oscillations in certain frequency bands are often correlated with conscious awareness. Micah's New Law of Thermal Dynamics suggests that once the wave differential (new stimulus) has propagated through the circuit—i.e., has been fully “computed”—the collective phase of the oscillation has changed to integrate the new information.
“Awareness” could be viewed as Entification from the ensemble finding (through repeated signal exchanges) a new coherent state that incorporates the novel input. This is much like a system reaching a new equilibrium that reflects the perturbed conditions.
Thus, in a tonic oscillating neural group, each “phase wave differential” introduced by a stimulus acts as a small disequilibrium. The iterative, wave-like signal exchanges that ensue are real computations—they successively update neuronal firing states until the network settles in a new equilibrium (or quasi-equilibrium) that may correspond to the network’s “representation” (and potential conscious awareness) of the stimulus.
3. Core Mechanic: Phase-Wave Dissipation Yields Awareness
Putting it all together, here is the core mechanism of how an oscillating group of neurons becomes aware of an incoming signal under Micah's New Law of Thermal Dynamics framework:
Equilibrium Baseline: The tonic oscillating group is in a relatively stable synchronous state.
Disturbance / Input: A stimulus introduces a phase differential (energy, or information difference).
Iterative Signal Processing: Neurons propagate, integrate, and dissipate that difference—meaning the entire network’s firing patterns shift to incorporate the new input.
New Stable Pattern: If the disturbance is significant and the network has the proper connectivity/dynamics, the result is a new coherent oscillatory pattern.
Emergent Awareness: In many theories of consciousness, this new coherent pattern is precisely what we experience as “awareness” of the input.
This aligns closely with the thermodynamic idea that a system re-equilibrates after a disturbance. What’s new is that we’re calling out each interaction as a “computation,” and each wave of neuronal exchange as the real-time mechanism by which the brain (like a thermodynamic system) dissipates differences to reach a coherent state.
Micah's New Law of Thermal Dynamics in neural circuits:
Information is the difference (in firing rates, phases, or membrane potentials).
Dissipation is how repeated signal exchanges smooth out or integrate those differences.
Computation is the network’s iterative process of moving toward a new attractor (equilibrium) that embeds the new information.
Conscious awareness arises when the global oscillatory state fully incorporates the new signals, achieving a unified firing pattern that the system (and thus the organism) “knows” or “feels” at the macro scale.
Hence, the proposed Micah's New Law of Thermal Dynamics describes real, physical, and computational processes that are as valid in neural ensembles as they are in gases and other thermodynamic systems—each step in the network’s response is a genuine “computation” of signal dissipation, culminating in a newly integrated state we call “awareness.”
Background
I don’t believe in Emergence or Irreducible Complexity because I’ve been thinking about this concept of high phasic phase wave differentials dissipating in synchronized groups of tonic waves for 2 years prior. I had previously derived the essence of what today became Micah's New Law of Thermal Dynamics a couple of years ago, in the summer of 2022, and it is indirectly described in the context of neuroscience in my youtube videos, github posts, and my audio recordings published to recorder.google.com at that time (with some published material on the topic emerging late in 2021).
The essence of what led to Micah's New Law of Thermal Dynamics, and a theory of Phenomenological Consciousness, and a theory of Gravity that connects General Relativity to Quantum physics came about from thought experiments after doing many experiments with EEG and other brain computer interface devices over the years, I did these experiments to think about, study, and imagine how waves interact, from brainwaves in the brain, to particle waves in space.
This core idea came to me while thinking about the book “Rhythms of the Brain by György Buzsáki” and the book “Sync by Stephen Strogatz” both authors talk about Synchrony, but at different scales. Strogatz describes how clocks, fireflies and single neurons can synchronize, while Buzsáki describes how oscillating powerbands in the brain like alpha & beta represent the synchronized activity of many cells at once.
What I realized is that I could describe the process of clocks and fireflies that Strogatz was talking about as a dissipation of the difference between the signals being passed between the clocks or between the fireflies.
I realized while reading Strogatz’s book Sync that each signal that passed between clocks or fireless was a phase wave differential or a traveling wave that was different in terms of phase. Eventually the clocks would Sync, and eventually the Fireflies would sync, because they dissipated the energy difference between them. Strogatz laid out the concepts in his book, and I coined these terms while thinking about what he was saying.
Strogatz also talked briefly about Sync happening between neurons, but I went further and imagined how when combined with concepts of tonic oscillating groups of cells and high phasic perturbations that I was thinking about from Peter Tse’s work (mentioned below), that Differential synchronization could lead to neural renderings in the mind.
In summary Stephen Strogatz's Sync explores synchronization as the natural phenomenon where systems with rhythmic behaviors, such as clocks or fireflies, align their phases over time. This synchronization arises from interactions between the entities, gradually leading to harmonious operation.
While Strogatz focuses on the mathematical and physical principles underlying synchronization, he leaves much of the application to neural systems for others, such as György Buzsáki and Peter Tse, to explore.
The concept that synchronization dissipates energy differences is my own interpretation, inspired by Strogatz’s work but not explicitly stated in Sync. I extend this idea through the lens of "phase wave differentials," proposing that the dissipation of phase differences forms the basis of synchronization in both physical systems and neural renderings in the mind.
These interpretations represent a synthesis of Strogatz’s foundational principles with additional insights from neuroscience and my own conjectures.
When I read about Latent Diffusion Networks, right after Stable Diffusion and Midjourney first appeared to the world, I realized a process through which phase wave differentials could oscillate together to bind to form stable representations.
I realized from my study of Photogrammetry, Light Field Imaging, Diffusion Tensor Imaging, Tomography and the Fourier Slice Transform that oscillatory binding could allow the brain’s neural arrays to reconstruct 3D renderings of sensory input data, from your sensory organs, and that your brain would be able to see these patterns as they perturbed the large groups of cells that were synchronized with high magnitude low frequency waves.
Peter Tse’s book allowed me to consider how the high magnitude low frequency waves had different properties, and I imagined that they could represent an idea from mythology & religion called the ground of being, or the formless space of the mind in which rendered patterns arise.
I imagined that the high phasic patterns, that come from sensory input areas, might bind together with oscillation, to become neural renderings that are seen by other neural arrays, and that as neural arrays received and dissipated the high phasic perturbations in order to return to a status that was closer to equilibrium, this would represent an oscillatory noising process (evoking the principle concept of Latent Diffusion Networks), where the brain would through LTP store memories in the learned configurations of dendrites that were activated by the brain’s electromagnetic phase wave differential oscillations.
Core Premise
At the same time, I was thinking about phase wave differentials perturbing tonic oscillating synchronization. I was also thinking about the interesting phenomenon called 1/F in EEG Medical Imaging, how, in the context of EEG, amplitude seems to always be inverse to frequency. This in contrast with the explanation in physics that it is distance or duration or time, (but not amplitude) that is explained as having an inverse relationship with frequency in a sense.
The wave shape is defined by amplitude, distance (or time), and frequency, and the amplitude & distance are both part of the waves magnitude, I realized that if a wave had a fixed energy then any change in the magnitude could result in an inverse change in the frequency as long as all other factors related to the wave such as voltage, capacitance, energy and charge remain constant.
What exists is a generally inverse relationship between magnitude and frequency, in any kind of wave, most often observed as amplitude inverse to frequency in the brain, and duration inverse to frequency in physics, as long as all other properties that might influence the wave shape remain constant, such as voltage, resistance, capacitance, and charge.
Of course if the voltage of the wave does increase it could increase the magnitude and the frequency at the same time, so its not like the wave shape is always confined to this inverse relationship, but as a general principle this idea led to an idea for how to unify gravity waves and quantum physics.
I initially called my idea Quantum Gradient Time Crystal Dilation, but then revised the name to Dark Time Theory because it provides validation for MOND, and a valid alternative to both Dark Time and Dark Matter, as well as being able to explain the Hubble Tension data.
When I thought of the generally inverse relationship between magnitude and frequency, when other properties like charge remained constant, I could think of the universe as having particle wave configurations that had high magnitude & low frequency, and this in my mind was connected back again to neuroscience with the descriptions of tonic waves described by Peter Tse in his book “The Neural Basis of Free Will: Criterial Causation.”
I imagined some of the waves in the universe might have particle wave configurations that had high frequency and low magnitude, and if the properties of voltage, resistance, capacitance, and charge remained the same then these two wave shape types might define spacetime, particularly when it comes to time dilation at the quantum scale.
It might be, I thought, since mass and energy were differentiated by time in Einstein’s Equation, that there was also some sort of relationship between the spacetime field and mass that had some sort of inverse relationship. After all the void of space is an energy field right?
I imagined that where the density of mass & time frames was great, there would be a correspondingly great volume of empty spacetime around it. As the density of the mass and time increase, the volume of empty space might increase at the same rate relatively speaking. This would imagine that spacetime was a napkin, and where there was mass the time part of the napkin was crunched up, and the space part of the napkin was relatively stretched out.
I imagined that if we had more frames of time (or more waves of time, or a phase difference in the waves of space) around mass at the quantum scale that this might change the path of particle travel, because the particle might interpret additional frames of time as additional frames of space that it has to compute its trajectory through.
Neither an object nor a particle traveling in a straight line is expected to change its path without a force acting upon it, but I imagined that for a particle it maintained a straight path because the odds of deviating into another path were approximately cancelled out by even odds.
For example, let’s imagine a thought experiment where the particle could travel in 4 directions that deviated from it’s path, and the odds of deviation were roughly equal on all four sides, let’s say 25% each, then the particle wouldn’t deviate from it’s path.
However if we increased the number of time frames in the particle’s vicinity, such that for every time it computed the possibility of deviating from it’s path, it now had uneven odds in some particular direction, that would change the particles path.
Imagine you roll a dice to see if you are going to land in 1 of 6 squares, with each interval of time, and the way it works is one square comes up as a possibility 1 out of every 6 frames. Now we increase the density of time for 1 of the squares. So that in one frame of time 5 of the squares will come up to the particle as a chance place to move 1 time, and one of the squares will come up the particle with a chance to move eight times.
This is a metaphor to say that if time is more dense in some area, or if there are more time frames in one direction, then it is possible for this to change the odds of a particle’s random walk calculation in determining it’s path in the next step of it’s computation, because the analogy is that the particle or object has to consider the direction where there is more time more often than it can consider the areas of space where there is less time. More time means more space from the particle, wave, or larger objects perspective.
So we know that the particle, wave, or object is not going to change it’s path without another force acting upon it, but if there are additional time frames in some areas of space verses others, or additional time density in some areas vs others, then from the point of view of the particles energy, a straight line is now a curved line.
We can test this theory in a number of ways, one way is by attempting to more accurately predict the curvature of light around galaxies. General Relativity isn’t that accurate when it comes to Gravitational Lensing but perhaps this theory and the equations that resulted may lead to better Gravitational Lensing predictions.
My Quantum Gravity equations that resulted from these thought experiments tend to center around modifying a particles energy in the presence of gravitational fields, because the additional time density around large masses would perturb the particle’s path with the phase differences in the gravitational wave representing a relative difference in time.
I ran these initial ideas through many AI programs, over and over, hundreds of times, for almost two years, in programs like ChatGPT, and I produced a great number of Equations, representing different ways to modify over 20 great physics equations with the concept of time frames as a unification of gravity and quantum physics.
Thank you, let’s connect and talk.
Thank you for taking the time to read about my Dark Time Theory, Self Aware Networks Theory of Mind, and proposed Micah's New Law of Thermal Dynamics. I believe these interconnected ideas illuminate a single fundamental mechanism—wave-based signal dissipation—that underlies everything from neural consciousness to gravitational effects in spacetime.
Should you wish to delve deeper, I’ve published extensive notes and videos on my GitHub repository and in my new book on Amazon. I’d be thrilled to discuss potential collaborations or hear your feedback on these hypotheses. I hope this perspective can stimulate dialogue among scientists interested in bridging the gaps between thermodynamics, quantum mechanics, and the nature of consciousness.
Can we talk together about my work and how it connects to the work of more established scientists?
Can you help me bring attention to my work since it is addresses these fundamental core scientific topics in novel new ways?
The neuroscience part of this letter is what the new book is about, but it’s also what I described in video in the summer of 2022 on youtube, and I have published a lot more ideas that came out of this core idea on my github which anyone may explore for free.
I’ve been publishing my notes on github, I’ve spoken at length in youtube videos on youtube explaining parts of this concept, and I’ve recorded many audio files published to recorder.google.com and linked to github about these ideas, also I recently wrote a book that I published on Amazon Kindle and in paperback on Amazon.com that describes these ideas in the context of neuroscience. In addition I have published many articles on my substack at SVGN.io on these topics.
Here in this article are links to the book as well as links to my youtube videos where I talk about the NAPOT theory which is the core thesis of the Self Aware Networks theory of mind, the gravity theory is spread across half a dozen articles and much more of the gravity theory has been published to my github, I will link those articles below this article:
https://www.amazon.com/dp/B0DLGBHJHG