Memes as Precognitive Neurological Wave Functions

So how does a piece of information programming work inside the human mind?  What is the basic processing unit? The answer is a wave function.

Inside the brain, we have many types of cells, but the main cells that we are sure have something to do with the thinking processes of the brain are Neurons.

Neurons have a couple components, and the ones we are interested in are Dendrites and Axons.

Think of them this way, a Dendrite is a receiver of signals from other Neurons.  Neurons can literally have hundreds of thousands of Dendrite endpoints.  These even include endpoints on our skin, which receive input from non-Neuron cells to sense touch and temperature.

An Axon, on the other hand, comes out of a Neuron only once.  While there are some instances of Neurons without Axons, that transmit over their Dendrites, the Axon can be thought of as a transmitter of signals.  Axon’s can branch, so that the Neuron through a single Axon can send its signal to thousands of locations or Dendrites.

These connections are called Synapses, the connection from one Neuron to another.

So, A Neuron receives signals from the Dentrite, and when that signal reaches a certain point, it starts firing signals out of it.  It can also change the rate of firing, usually as a power function.  In other words, at pressure X, it may fire signals at X^2, or at 1/X.

This, of course, depends on the functioning/programming of the Neuron.  Now, because a Neuron can alter it’s output based on input, it is able to perform more than simply a binary switch.

Binary switches are how computers work.  The iPhone 6, for instance, has 2 billion transistors.  This means that it has 2 billion binary switches.  They can either be in state 0 or 1.  That’s it.  There are no inbetweens.

But a single Neuron can be in multiple states of “gain”.  If you want to read the scientific paper on this theory, check out http://journal.frontiersin.org/article/10.3389/fncom.2014.00086/full

Now, a Neuron is either on or off, which is binary.  But a Neuron can also change the frequency of impulses that it sends.  In other words, the rate at which the Neuron fires is not set, but is variable.  Depending on the simple programming of a Neuron, it will alter multiple inputs into a single output of variable firing rates.

In other words, a single Neuron can do complex math, including power functions.  This makes the lowly transistors on your iPhone look down right stupid.

How much more power?  The average Neuron is, on average, connected via 7000 synapses.  Wow.
The human brain has about 100 billion Neurons, and upwards of 500 trillion Synapses.

We can think of Neurons as logic circuits.  In older technology of the 1970s and 1980s, this was literally a single chip, which might have 8 inputs and 1 output, though chips allow for many configurations of inputs and outputs.  The chip usually performs a single function.  Take the inputs from 4 bits, add it via binary math to that of the inputs of 4 other bits, and spit out an answer.

But a Neuron has two very big differences.  First, they average 7000 synapses per Neuron.

This could be 6999 inputs and 1 output, or it could be 1 input and 6999 outputs, or any variation in between.  What this does, though, is allow for much more complex math functions within the Neuron.  And when we are speaking of physics, we are often speaking of wave functions.

In fact, we’re not just talking about 7000 inputs, but inputs with adjustable Gain, or frequencies.  So, regardless of what math a Neuron does, we could could the Synapse as the “super transistor” because we’re talking about channels.

Think of it this way.  Each Synapse is like a radio station, only with a single volume.  It never gets louder or softer, but is always at the same volume.  The best analogy is Morris Code.  Only, it is not operating in dots, dashes, and pauses.  Neurons can have dots, faster dots, faster faster dots, faster faster faster dots… (and so on), and pauses.

In other words, a transistor either sends a dot or a pause, 1 or 0.  But a Synapse can send 1, 2, 3, 4, 5… (and so on) and 0.

The number of frequencies that a Synapse can carry is a set of numbers.  For a transistor, it’s binary.

But how many frequencies?  This is a great, and critical question, for understanding the power of a human mind.

Before we begin, understand that another power of the human mind is a more complex communication channel using different speeds and technology.  For instance, if Synapses were to fire 1000 times faster than a transistor, then the synapses are already 1000 more powerful than modern transistors, even if the number of them were the same.

So, we’ll assume a static speed for all communications.  In this model, we’re looking at it as though all devices ran at the speed of light, for instance.  Does this change intelligence/computation?  No.  It changes the “speed” of intelligence/computation.  While this does make a difference, even if a circuit took 20 years to “form a sentence”, we would still consider this intelligence.

What we’re looking at here is the power of the system, not the time.  And as we will see, it’s pretty dramatic even when we don’t look at speed.

How Powerful Is The Brain?

First, let’s assume that they are binary, and compare them to the iPhone 6.

An iPhone 6 is 2 billion binary transistors.

If the brain were just binary, it would be the equivalent of 100 trillion transistors.  That means that a human brain is at least the equivalent of 2,500,000,000 iPhone 6s.  That’s 100 trillion to the binary power of 2 (the brain) divided by 2 billion to the binary power of 2 (the iPhone).

Interesting.  The brain is at least as powerful as 2.5 billion iPhones.  But, the brain is not binary.  It is at least ternary, or three. This means that each transistor can be in three states, 1, 2, and 0.  So what’s a ternary comparison?

(100 trillion ^ 3)/(2 billion ^ 2) = 250,000,000,000,000,000,000,000

Or, the human brain is as powerful as 250 thousand billion billion iPhone 6.  Holy shit.  And that’s if Neurons are capable of just zero, low, and high states.  But, let’s assume that it can do more, lots more.  Let’s assume that a Neuron responds to a power function of 1/x, and has 9 inputs.  This means that it could use 0, 1/1, 1/2, 1/3… to 1/9.  This gives it ten output states.  Remember, Neurons could have 6999 inputs and 1 output, giving it 7000 output states.  Unfortuntely, you have to realize just how large that number is.  It would take me days just to write it out.  So let’s just assume 10 to show how quickly this grows, shall we?  I don’t have days to just sit writing a single number with lots of zeros.

(100 trillion ^ 10)/(2 billion ^2) = 2.5 x 10^121.

That’s 25 with 120 zeros after it.

That’s 25 trillion trillion trillion trillion trillion trillion trillion trillion trillion trillion iPhone 6.

Obviously, that number is just unintelligible.  But, I think we can safely say that your iPhone will never be capable of intelligence.

So let’s compare it to the world’s currently most powerful supercomputer.  In 2012 IBM simulated a brain, sort of.  http://www.kurzweilai.net/ibm-simulates-530-billon-neurons-100-trillion-synapses-on-worlds-fastest-supercomputer

It operates 1542 times slower than a brain.  It also has 10 times fewer Synapses than a three year old child (5 times less than some adults).  And, unfortunately for IBM, still works on binary.  To achieve even a binary equivalent, they would need more than 1,500 of these supercomputers just to simulate a single brain.

And even then, there’s no more intelligence there than that of a comatose monkey.

But, when we look at how computers work and how brains work, we see something appear out of multiple gain states that is harder to picture in a binary system.

Imagine a ball bouncing on the waves.  At the top of the wave, it’s a 1.  At the bottom of the wave, it is a zero.

A computer doesn’t shut off to represent zeros, it simply has low voltage and high voltage states.  And it fluxuates to switch back and forth.  It forms waves and captures the state at either high or low.

Neurons work the same way using different mechanisms.

In a computer wave function, we find that complexity is a result of the fidelity of the wave function (the number of points we observe) and the number of waves.  There are two ways for increasing complexity.  We can either increase the fidelity and observe more points, or we can create more waves to observe.

The total number of points observed in the system, between fidelity and number of waves, results in a direct arithmetic result in complexity.

The other factor, of course, is speed, which we have not covered here.  It would be theortically possible to increase the speed to the point of only needing a single wave or a binary level of fidelity.  The wave could come at an infinite speed or we could have infinite fidelity of a single wave, so long as we have an infinite speed of either, we could have infinite computations.

In our cases, we do not have infinite speeds in either fidelity or waves.

But within a brain, each Neuron is a wave function device.  It is generating waves and computing waves.  And the results, the fidelity, occurs in the synapse, when the wave function collapses into the observed result.

What are the building blocks of neural programs at their most basic form?  They are wave functions.

Variable waves move into Neurons, a wave function is applied to it using basic electrochemistry, and a wave function is output.

All memes must thus be based on waves.  The output of these waves, outside the body, are functions of Neurons on the body.  Neurons receive, almost directly, all sensory input and transmit, also almost directly, all physical manifestations of muscle movements.

Within the body, all paths point to the Neuron.  One could think of the body as having two parts, Neurons and those things that support Neurons.

Memes are the software of the Neurons, written in the language of wave functions.  And at the basic level, the single Neuron, they are not very mysterious.  Mostly known and well understood chemical reactions in waves, even in those of worms, perform their actions based on mathematics.

Even at this level, Neurons are powerful enough to sustain animal life for its host.  And yes, even at this level, memes exist.

If we think of memes as software, a human societal meme might be the Windows 10 of memes.  While the precognitive memes of the worm might be the alarm clock circuitry of memes.  The level of programming may be vastly different in complexity.  But the physical fundamentals remain the same.

The human might have billions of Neurons, while a certain worm might only have 302 Neurons.  But they are both operating on the same biological wave function devices.

Software in computers builds on binary math, which is built up into more complex math, and built up into a full machine language, and built up into higher languages.

Such is the same with all software.  It builds, brick upon brick, into more complex machinery with more abilities.

At the bottom of the neurological programming building block, we have Neurons with simple wave functions.  Above this, we have basic machine language.  But what does this look like?

It turns out that research shows action before cognitive thoughts.  Around 95% of the time, we are making up stories to explain our automatic actions.  We only actually think in a cognitive manner about 5% of the time.

What this means is that 95% of our actions are automatic or autonomic.  They are either learned and run precognitively, or they come preprogrammed and run precognitively. Learned precognitive actions are like walking and drinking from a glass.  Preprogrammed precognitive actions are mimicry of people around us, breathing, our heartbeat.

What is interesting, and possibly dangerous, is that many if not most of our memes are precognitive.  This, of course, makes perfect sense with complex wave functions.  The only things that build these wave functions are biology.  We have no conscious control over writing or changing these wave functions or in writing them in more complex ways.

Luckily, our brain comes with neat functions already built in, which we can actually observe.  One of these is observed with the Wason Selection Task, https://en.wikipedia.org/wiki/Wason_selection_task

Given a purely logical problem, we suck at solving it.  But give it the context of catching a cheater, and we excel at it both in speed and correct solving abilities.

We have a collection of wave functions for solving specific human related problems. And when problems are put into a truly human context, we solve them brilliantly.

We have complex wave functions for recognizing human faces, emotions, and for recognizing our name when it is spoken in a noisy room.

Each of these prebuilt functions have direct Darwinian explanations for survival.  Some of the variables may be left out, such as our name, but they are built with the ability to fill in these gaps later on.

Unfortunately, these wave functions are precognitive, which means that we have little control over them.

Would you like to catch a spy with a social experiment?  This is quite easy if you have the technology.  Construct a large PET scanner in a public area, maybe using micrometer scanning technology.  Have a speaker system in a public square filling it with voices calling out names of known spies, but mashed together that nobody can make out any of the names clearly.

Once the system projects “James Bond”, the real Bond cannot stop his brain from hearing his own name clearly called out.  It’s almost as though the speaker system said this name to him alone, because his brain filters all information to hear this information specifically.

Once he hears the name, certain portions of his brain will light up like a Christmas tree.  The system, seeing this reaction, could repeat the name again, maybe in a different voice, and record the reaction.

Having done this, Mr. Bond cannot hide.  It doesn’t matter if he’s wearing a disguise or sitting in the back of a car with tinted windows.  If he hears his name, his brain will register it, and this change can be registered by monitoring equipment.

Of course, we’re assuming that the square isn’t filled with people calling out the names of people around them.  But given enough time and random timing, we have an effective spy catching system.

Wave functions, therefore, can be triggered precognitively, and we have very little control over them.  This leaves us vulnerable to manipulation.

Want to manipulate yourself?

Want to force my body to start taking a defensive posture? With a simple sound, I’ll stop talking.  My heart will skip, and start pumping hard, and adrenaline will start pouring through my body.  My focus will come down to a pinprick, and my breathing will slow to deep and rhythmic breaths.  My muscles tense up, and then go loose, ready to react.

What could cause such a profound automatic reaction, but only in me and not most others?  At least, not in most others to the same degree in me.

It’s quite simple.  I grew up in the martial arts.  I spent more than a decade honing my body to fight both with and without weapons.  As such, I also love martial arts movies.

I also love music.  And I find that music affects my mood greatly.  This makes me susceptible to musical memes.

And one of my favorite movies is the Kill Bill Series.  Start playing the song “Don’t Let Me Be Misunderstood” by Santa Esmeralda, and my body reacts like it’s getting ready to take down an enemy.  This music is the start of one of the most beautiful sword fight scenes at the end of Volume 1.

This might be painfully annoying to most people, I’ve taken advantage of precognitive reactions to music for years, as do many people.

It’s why people listen to music while working out, to change their mood, and to elevate their biological functions.  They are tapping into a combination of complex wave functions:

Autonomous Precognitive Triggers + Conditioned Precognitive Triggers and Responses

What this results in is quite fantastic.  These happen to us all the time, but I love sitting and starting a song with my mind clear, and feeling my brain go firing off in a way that is both predictable, yet thrilling.  It gives me goosebumps, a precognitive response. Just to note, I’ve learned to cause this response through conscious intention… a little eye roll up, a mental image of cold electricity going over my scalp, and the skin across my entire body lights up with tingly goosebumps.

Another song I like to use to trip these cascades of precognitive autonomous responses is “The Grid (Remixed by The Crystal Method)” by The Crystal Method and Daft Punk.

The responses I’ve worked on associating with this trippy little piece is an association to it with meditation and rapid response thinking.  I can literally feel myself shaking in nervousness as the song progresses, with my mind clearing of all external thoughts and again, gaining extreme focus on what is in front of me.  The basic condition, here, is to bypass conscious decision making, and to work, mentally, in a precognitive fashion.

I’ve found this to be a wonderful way to write in a stream of thought method.  The beat drops at the 3 minute 11 second mark, and I feel the Neurons literally light up my skin and carry me off.

I find that at such moments, my level of output and reaction time with my fingertips on a keyboard takes my normal typing rate from about 70wpm to over 150wpm.

Cut the music, and my rate of typing slows to a virtual crawl, and the distractions of the outside world flood back in.

Now, granted, all of this sounds like woo woo bullshit.  It sounds like spiritualism or tapping into some Freudian subconscious area.  Or worse, it sounds like trying to create superpowers from a mind where no such superpowers exist.

But let me stop all readers at that point.  There’s nothing woo woo about this at all.

What there is here are two things.

First, the realization that you can flip switches in your body and mind through wave functions, aka memes.  And the most interesting ones for me are the precognitive wave functions.

Second, this can be done through conditioning, to some extent.  Unfortunately, this conditioning looks rudimentary.  It’s like beating a computer with a hammer and hearing it make funny noises.

What it lacks is sophistication and finesse, mostly due to missing layers of abstraction in the areas between the basic wave functions of neurology and conscious thoughts of the mind.

There is a whole lot of unknown areas in between.  We are like computer users who are only allowed to see crayon drawings of the inside of the computer.

I can cause biological reactions through music, which is a pretty well known and studied phenomena.  See “Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion” by Anne J. Blood and Robert J. Zatorre (2001) Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4 http://www.pnas.org/content/98/20/11818.full

So, in order to start moving upwards, we’re going to have to speak in more generalized terms above the abstraction layer of wave functions.

However, we know that these wave functions are extremely powerful, surpassing our current supercomputing capabilities by orders of magnitudes.  By combining them, we find that we can construct something larger and more complex, which we call software.

We’ll start analyzing what this software looks like, and see what the basic code looks like