On Computing the Brain and Mind

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?

gib

No need to respond to this post - I just want to expand a bit.

Now if we were to consider how the computer models GO compared to a brain I think that we would find the two very different. Our mind however would be similar and just a bit slower. I am still claiming that computers are a result of the mind and not the brain - but this can get ambiguous of course.

Man this is so cool!

Neurons themselves are quite a bit different to logic gates or even combinations of them. These gates are the AND, OR, NOT, NAND, NOR, EXOR and EXNOR gates. Digital circuits are of course modeled using combinations of logic gates. When we are building a computer we are building a mass of gates - in many ways different to the brain. We can put software on the hardware. We have to do conversions from binary all the way up to English with many layers in between - these are called abstractions.

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not >> think excitatory and inhibitory <<). It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time.

The perceptron algorithm dates back to the late 1950s. Its first implementation, in custom hardware, was one of the first artificial neural networks to be produced.


A diagram showing a perceptron updating its linear boundary as more training examples are added.

I think a biological neural network would make those complexes happen if they were needed - I mean the people who invented the NOT gate. AND, OR, NAND, NOR, XOR, XNOR must have had the networks in their brain. I know I would have them uploaded and installed in my brain :evilfun:

gib

Or however else one wants to divide things up . . .
. . . obviously it is these divisions that we work with when we discuss these sorts of things . . .
. . . the divisions are a matter of convenience and . . .
. . . standards are just divisions that we agree upon . . .

I like it - it is not quite what I was going for but it is perfect in a way.

Standards are good but we should not be too scared to make new divisions.

Standard divisions are more convenient for communicating to others who understand those standards.

We should now remember though, don’t take anything too literally because one never knows when one has a breakthrough because of an open mind.