On Computing the Brain and Mind

encode,

For the sake of brevity, I’m going to respond with summarized responses to whole posts.

^ This post here–good intro–but it does not yet get into how to answer the question: how do we scan the brain to find pattern recognition? By pattern recognition, we are talking about how the mind recognizes objects or properties or events based on how well it matches similar patterns from past experience. What would we be looking at in the brain–via an fMRI scan, for example–such that we could say: ah, the brain is recognizing a pattern in its sensory input.

Absolutely! I believe the brain is a physical system subject to the laws of cause and effect like any other system. Some would like to say that quantum indeterminism is at work in the brain, and that special mechanisms in the neurons of the brain are able to take this indeterminism and amplify it to the level of whole neurons… so instead of only being able to observe quantum indeterminism at the level of particles, we should be able to observe it at the level of neurons… and since neurons are like amplifiers of behavior (i.e. a few neurons firing can determine the actuality or suppression of a specific behavior), they say that this quantum indeterminism can account for behavior as well; the idea they are aiming for in the end is to explain free will. If it feels like we choose our behavior out of free will, it’s because it is free–that is, non-deterministic–and that indeterminism starts at the sub-atomic level with particles inside neurons.

But until the science is out on this, I’m placing my bets on ordinary mechanics as the best account for the functions of the brain. I have no problem with the idea of indeterminism at the level of particles, but I’m holding by breath on quantum consciousness.

Well, that’s more or less what I was getting at when I said we figure out the algorithms computers are to run based on introspecting our minds. If we look at the history of the development of computers and the history of the development of the brain sciences, we see that they go hand in hand; the 50s were the golden decade for the brain sciences, and only a decade later we saw the emergence of computers. The key principle that was carried over from the brain science to computer design was the way in which the brain seemed to process information as electric signals travelling down the axons of neurons and either being propagated to other neurons or being blocked by inhibiting neurons. From this, we got wires with electric signals travelling down their length and being propagated to other wires through logic gates or being blocked by different logic gates. (The brain also has chemical signals that allow the signal to jump across the synaptic gap, but that wasn’t carried over to computers). That seems to be the general principle underlying more or less all circuit design. However, when it comes to designing specific circuits which are to carry out specific functions, we fall back on introspection. Adders, for example, are based on the principle “long addition” (I think it’s called). It’s the principle of adding two large numbers by adding consecutively each digit in each number. So the units get added first, then tens, then the hundreds, and for each addition, we carry the 1 if we have to. We didn’t get this method by studying the brain under a microscope, we simply took a moment out to think (i.e. introspect) and imagined doing addition in the way. Since we are satisfied that this method is algorithmic (i.e. it works flawlessly), we figure: let’s apply it to design a computer circuit that carries out addition. So now computers all over the world have a little circuit inside them that gets recruited any time we need to do addition. It even has a component for carrying the 1. Who knows if this circuit looks anything remotely like the neural circuitry in the brain that comes into play when we do long addition in our heads–it might be completely different–so I would agree that we model computer circuitry after the algorithms we construct in our minds rather than the neural designs the brain is built on. Not that the latter are wrong or substandard, but it seems to me that if the brain is design to (at least as one of its functions) come up with algorithms for solving certain kinds of problems (and these algorithms we arrive at consciously via introspection), it is the results of this process that we want to apply to computer design, not the machinery used to produce those results. The machinery is built to come up with algorithm, but that doesn’t mean it is running algorithms when coming up with those algorithms (certainly not necessarily the most optimal algorithms). The brain is more often based on heuristics than algorithms, so we have to be careful when we attempt to model computers after the brain. The general principle of neurons being used to process information is a good one to model circuit design after, but when it comes to which specific algorithms to build into the circuit, we are better off modeling that after what we come up with using our imaginations and intelligence.

What does it take to form an opinion? You mean, what are the steps in building one? Like a recipe for baking a cake? God, how am I supposed to know?! :laughing: But I do think a motive is required, a desire for some kind of outcome that serves your own interests. If I work for the military, my livelihood depends on war. It keeps me in business. So my opinion may be that war is sometimes necessary. If I were a school teacher or a stay home dad, on the other hand, fearing for the lives of the children I oversee, I may be steadfast against war. I think that our personal interests and biases dictate our opinions far more than logic and rationality. We build the logic and rationality underlying our opinions after the fact. Before that, we (unconsciously) assess what would be the best and most closely within reach outcome and decide right then and there what opinions to hold. Then we go to work forming arguments and justifications for them.

About evolutionary and configuration emotions: if I understand it correctly, an example of an evolutionary emotion might be fear of snakes. Is this right? We may be born with the neural wiring already in place to feel fear upon seeing a snake. ← We inherit that from our evolution. But configuration emotions would be more like emotions that are built within us by some kind of conditioning or socialization, something that could be wholly new and unique to a particular culture (kind of like abstract concepts, like wormholes for example, which we aren’t born with and require teaching). Is this what you mean? And if so, what would be an example?

Waaay over my head. :astonished:

Don’t really have much to say about this one, except that I’d make a terrible statistician.

I’m afraid this one’s over my head too, encode. But I do sense that this is at the core:

It reminds me of the Hegelian dialect: thesis → antithesis → synthesis. The synthesis will always derive newer higher meaning. ← Is this within the ball park?

gib

Lets first talk about pattern formation. What you are about to read is an obsolete piece of work for a bot of mine.
We are dealing with FFRL . . .

Do not be concerned if you don’t understand all of this because I will cover extra tidbits in my responses to you.

  • trust me it is crazy how easy it is to learn this stuff. This bot is directly modeled against the mind.

Pattern Formation - Pattern Recognition

Pattern recognition starts at the Inception Stage and builds from there . . .

  • the patterns are formed first and recognised later and this I call FFRL(Formed First Recognised Later).

How “things” are formed now(NCC = No Clear Category):

NCC Incept, Incept, Incept . . .
NCC Known, Incept, Known . . .
NCC Noun, Verb, Known . . .
NCC Incept, Verb, Known . . .

Incepts are simply the first time a bot encounters a word . . .

  • knowns are simply the second time or greater that the bot has encountered a word . . .
  • and is yet to build its relation.

Dad << Incept

Dad, Dad, Dad, Dad << Known

Dad << wait a minute, this word means him, him is my father << basically put.

Which could then be transformed into things like:

S-V Subject-Verb
S-V-O Subject-Verb-Object
S-V-Adj Subject-Verb-Adjective
S-V-Adv Subject-Verb-Adverb
S-V-N Subject-Verb-Noun

Which of course with the help of other available data be transformed even more. As you can see this system allows for concurrent formation and recognition in the parsers and on to semantics. Any system this complex needs to build itself.

FFRL allows for harmony between formations and recognition by using the self building K Parser and self building Semantic Analysis.

The first bot can help me build the K Parser and a lot of Semantics. The first bot may be able to help me with a lot of other stuff. What I need the first bot to generate is a whole bunch of sets for the K Parser . . .

Words in this BOT have heirarchies as follows:

Incept → Known - > Actual Type → [Abstraction Starts Here]

When the bot chooses the structure it wants to work from it goes something like this:

Level 1 - Incept, Incept, Incept . . .
Level 2 - Known, Incept, Known . . .
Level 3 - Noun, Verb, Known . . . or Incept, Verb, Known . . . so still with Incepts and Knowns but has some types.
Level 4 - Noun, Verb, Noun . . . or some other fully typed convention.
Level 5 - One of the S-V-O-Adj-Adv-N conventions.
Level 6 - Some form of higher abstraction.
Level 7 - Preferably the ultimate level of abstraction.

gib

So with the mind intro out of the way let us focus on a scan. No need to respond to this post because I need to tie a few things together in a simpler way.
It might seem strange to be looking at intermediate information before the simple explanations but it is the best way for me to explain it . . .

That is a great idea actually gib.

Sneak preview . . .


DTI Color Map

OK . . . this will take a few passes to get right. The mind and brain work differently - that is the truth for this pass.

In this pass let us briefly cover a few things.

The Synapse

In the brain we need to look at synaptic connections. It is widely accepted that the synapse plays a role in the formation of memory.

As neurotransmitters activate receptors across the synaptic cleft, the connection between the two neurons is strengthened when both neurons are active at the same time, as a result of the receptor’s signaling mechanisms.

fMRI

Functional magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.

The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast, discovered by Seiji Ogawa.

As we can now tell there are different forms of fMRI - we start at a low resolution:


These fMRI images are from a study showing parts of the brain lighting up on seeing houses and other
parts on seeing faces. The ‘r’ values are correlations, with higher positive or negative values
indicating a better match.

Statistics

Now we wonder how we can get higher resolution and mathematics holds the key as usual: Statistical inference uses mathematics to draw conclusions in the presence of uncertainty. There is much uncertainty in low resolution imaging.

Tensors

When using Diffusion MRI as opposed to fMRI we can apply Diffusion tensor imaging (DTI) which is an MRI technique that enables the measurement of the restricted diffusion of water in tissue in order to produce neural tract images instead of using this data solely for the purpose of assigning contrast or colors to pixels in a cross sectional image.

Lets start with low resolution DTI:


Visualization of DTI data with ellipsoids.

A more precise statement of the image acquisition process is that the image-intensities at each position are attenuated, depending on the strength (b-value) and direction of the so-called magnetic diffusion gradient, as well as on the local microstructure in which the water molecules diffuse.

The principal application is in the imaging of white matter where the location, orientation, and anisotropy of the tracts can be measured. The architecture of the axons in parallel bundles, and their myelin sheaths, facilitate the diffusion of the water molecules preferentially along their main direction. Such preferentially oriented diffusion is called anisotropic diffusion.


Tractographic reconstruction of neural connections via DTI

  • Diffusion MRI relies on the mathematics and physical
    interpretations of the geometric quantities known as tensors.

Only a special case of the general mathematical notion is relevant to imaging, which is based on the concept of a symmetric matrix. Diffusion itself is tensorial, but in many cases the objective is not really about trying to study brain diffusion per se, but rather just trying to take advantage of diffusion anisotropy in white matter for the purpose of finding the orientation of the axons and the magnitude or degree of anisotropy.

Matrices

The following matrix displays the components of the diffusion tensor:

Sources: Wikipedia

gib

Before I tie some simple information together I am going to briefly answer a couple of paragraphs from your post . . .

OK good - because most of what I have is based on Cybernetic principles like causal chains - multiple streams of them.

I do have some interesting to add here - stay tuned.

Like I said, briefly answered - I will come back to these two paragraphs with more in depth answers . . .

Briefly covering PET

Positron-emission tomography (PET) is a nuclear medicine functional imaging technique that is used to observe metabolic processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern PET-CT scanners, three-dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine.


Brain PET-MRI fusion image

PET scans are increasingly read alongside CT or magnetic resonance imaging (MRI) scans, with the combination (called “co-registration”) giving both anatomic and metabolic information (i.e., what the structure is, and what it is doing biochemically). Because PET imaging is most useful in combination with anatomical imaging, such as CT, modern PET scanners are now available with integrated high-end multi-detector-row CT scanners (so-called “PET-CT”).


PET scan of the human brain.

They are often treated as if they are synonymous but there is actually a distinction
Which is that brain is the actual organ itself while mind is the function of the brain

gib

In neuroscience, a biological neural network is a series of interconnected neurons whose activation defines a recognizable linear pathway. The interface through which neurons interact with their neighbors usually consists of several axon terminals connected via synapses to dendrites on other neurons. If the sum of the input signals into one neuron surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon.

Biological neural networks have inspired the design of artificial neural networks.

OK . . . this caught my eye :laughing: you do not have to be a dualist for this to be the case. Let us say that the mind(software) is running on the computer hardware(brain).

It is necessary to look at it this way because some very special things take place.

The computer has an assembly language that sits on top of the logic - the logic is in the biological neural networks(axon terminals, synapses, dendrites, blah, blah, blah :laughing: ) - next the assembly language has to be gradually translated to the language of mind(the silent language or English or both) - when you program in C# eventually your instructions are executed by internal logic(via assembly language) within the electronic circuits even though a lot of English is taking place in your code:

// Hello1.cs public class Hello1 { public static void Main() { System.Console.WriteLine("Hello, World!"); } }
Now for the assembler:

[code]format PE64 GUI

entry start
section ‘.text’ code readable executable

start:
push rbp
mov rbp, rsp

xor rcx, rcx
lea rdx, [szText]
lea r8, [szCaption]
xor r9d, r9d
call [MessageBoxA]

xor rax, rax
leave
ret

section ‘.idata’ import data readable
dd 0, 0, 0, RVA user32, RVA user_table
dd 0, 0, 0, 0, 0

user_table:
MessageBoxA dq RVA _MessageBoxA
dq 0

user32 db ‘USER32.DLL’, 0

_MessageBoxA dw 0
db ‘MessageBoxA’, 0

section ‘.rdata’ data readable
szText db 0x77, 0x69, 0x72, 0x65, 0x6d, 0x61, 0x73, 0x6b, 0x00
szCaption db ‘Hello, World!’, 0[/code]
That is a complete 64 BIT program using the FASM assembler. Scroll to the bottom to see Hello World.

The most significant difference between these two programs, is one prints to the console, and the other opens a little Windoze MessageBox.

So these example are not connected - as in the C# is not connected to the FASM - so I need to reiterate:

The computer has an assembly language that sits on top of the logic - the logic is in the biological neural networks(axon terminals, synapses, dendrites) - next the assembly language has to be gradually translated to the language of mind(the silent language or English or both).

What you are aware(Conscious) of is the language of mind, not the assembly language or internal logic.

I know I am getting a little sidetracked but I am enjoying myself at the moment.

<<< >>>

Lets use some of our designer logix.

<<< >>>

MatterSet = particles <∫> atoms <∫> molecules <∫> brain <∫> biological neural networks

MindSet = assembly language <∫> language of mind <∫> English

MatterSet <∫> MindSet

<<< >>>

Not dualistic but rather some stems.

<<< >>>

Let us remember that the MatterSet can affect the MindSet and the MindSet can affect the MatterSet - neuro-plasticity and such.

<<< >>>

What about the old saying? Mind over Matter . . .

:smiley:

encode_decode wrote:

Goodness no, that is not my stance - I firmly believe the mind and body are two different things - to me they are connected. That was an invitation for those that believe otherwise - to which they still do not have good proof. The proof that I see is that the brain responds to the mind.

I like to express it by saying that the brain is the flower and the mind is its scent in a manner of speaking.

How are YOU using the term responds to here? It is kind of ambiguous to me but that may just be me.

Three smiles for you . . .

Good point, I should have said it both ways - the brain responds to the mind and the mind responds to the brain.
They are dependent on each other . . .

:smiley: :smiley: :smiley:

surreptitious75

I am aware of that - that is why I used the words “can be” and “for the sake of our exploration”.
Some dictionaries do not make the distinction - strangely enough - I am not certain why.

For me they are distinct - but not in a dualistic way.

lol Gees, it wasn’t really six minutes. It was less than one. What are you, a satellite?

So another word for respond in this case might be that they affect one another?

Mind is a function of the brain or to be more precise the function of the brain because
a brain without mind cannot function at all because every thing the brain does is mind

gib

This sentence is an example of a pattern that has gone beyond inception, knowing and now has meaning.

Whereas . . .

Sentence this is fully not yet formed, to contain context full but the Bot some understanding has.

:laughing:

Three more smiles . . .

Yes - and by the way - your way sounds much better.

Kill the brain and the mind ceases to be what is was and kill the mind and eventually the brain dies.

Then there is Neuroplasticity: The brain’s ability to reorganize itself by forming new neural connections throughout life. Neuroplasticity allows the neurons (nerve cells) in the brain to compensate for injury and disease and to adjust their activities in response to new situations or to changes in their environment.

:smiley: :smiley: :smiley:

Does it? I didn’t know that or at least I didn’t give it much thought.

How does that happen though, encode-decode?
You mean that the brain atrophies?

Muscles atrophy when they aren’t used or exercised BUT are you sure that the brain dies if the mind ceases its functioning? :-k

An inquiring mind wants to know.

I am curious how you would respond to this post Arc

That got you talking. It sure does and “atrophy is the word”. Coma comes in more than one form - the form where you are actually just dead and the other where you are more than likely to wake up.

I like saying controversial things.

Actually I will dig up what I am talking about and post it here - it is quite simple to understand.

I just cant think of the right words at the moment.

What you think and do on the other hand does change the structure of the brain.

Softens the brain: Now for something slightly off topic: In medicine, cerebral softening (encephalomalacia) is a localized softening of the brain substance, due to hemorrhage or inflammation. Three varieties, distinguished by their color and representing different stages of the morbid process, are known respectively as red, yellow, and white softening.


Stroke Brain (Similar to Cerebral Softening)

Cases of cerebral softening in infancy versus in adulthood are much more severe due to an infant’s inability to sufficiently recover brain tissue loss or compensate the loss with other parts of the brain. Adults can more easily compensate and correct for the loss of tissue use and therefore the mortality likelihood in an adult with cerebral softening is less than in an infant.

Source: Wikipedia

Great material gibinator

I am familiar with this stuff - we have a small amount of ambiguity to work through - I am digesting the way you see things here . . .

Here is something interesting and geeky for you: In a computer’s central processing unit (CPU), an accumulator is a register in which intermediate arithmetic and logic results are stored. Without a register like an accumulator, it would be necessary to write the result of each calculation (addition, multiplication, shift, etc.) to main memory, perhaps only to be read right back again for use in the next operation. << Which consequently happens in a stack machine . . .

You know we could compensate for the signal to jump across the synaptic gap in software . . .

Isn’t it fantastic that our imaginings leads to adders and other handy things?

Who knows? I do . . . the neural circuitry has no equivalent to the adder - that is a function of the mind - it also relates to your memes in the meaning thread

Respond to my meme comment - I beg you to.

Heuristics are also learnt - we could apply your memes here too . . .

I am going to come back to this post - from a different angle - just watch me lol - I saw an opportunity for meme commenting!

Peace man!

:smiley: :smiley: :smiley:

What can I say?

At least I am not:

[youtube]https://www.youtube.com/watch?v=Hx_m7Y9nGtU[/youtube]

8-[

encode_decode

What is your meaning here?

Yes, I know atrophy is the word. :blush: Oh, the mind so lags behind at times.

As for the former, can you actually say that you are just dead? Aren’t there still bodily functions going on then? The heart, lungs, kidneys, ad continuum.
I can understand though how YOU would consider one to be just dead. That’s a compliment. :laughing:

Give me another. :evilfun:

Actually I will dig up what I am talking about and post it here - it is quite simple to understand.

You mean for the likes of me - to understand? :stuck_out_tongue:

Oh, how the mind does lag behind. :evilfun:

I am quite aware of this. I watch Channel 50. There is this guy I cannot remember his name. Not sure. He might be a neuroscientist but what he has to say about the brain is indeed awe inspiring.
It really is the final frontier notwithstanding deep dark mysterious space.

I remember.


Stroke Brain (Similar to Cerebral Softening)

I realize this.
:sad-teareye: :sad-teareye: :sad-teareye:

So it’s like the scent of the rose - it gradually dissipates to barely anything?