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.
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.
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.
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?
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
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.
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 . . .
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).
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 . . .
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.
Isn’t it fantastic that our imaginings leads to adders and other handy things?
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.
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.
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.
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!
encode_decode
That got you talking.
What is your meaning here?
It sure does and “atrophy is the word”.
Yes, I know atrophy is the word. Oh, the mind so lags behind at times.
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.
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.
I like saying controversial things.
Give me another.
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?
I just cant think of the right words at the moment.
Oh, how the mind does lag behind.
What you think and do on the other hand does change the structure of the brain.
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.
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.
I remember.
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.
I realize this.
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.
I would agree. The algorithms we programmed into computers in order to play GO are taken from those ancient Chinese thinkers who invented the game. Right? So we modeled it after their minds. Who knows what their brains were doing.
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.
True, I don’t think we’d find the equivalent of AND, OR, etc. gates in the brain, but we do have “gates”. There are two kinds of synaptic gaps in the brain: excitatory (allows the signal through) and inhibitory (stops the signal in its tracks). The inhibitory connections are like a rudimentary NOT gate.
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’m guessing this was the inspiration for inventing the NOT gate. AND, OR, NAND, NOR, XOR, XNOR ← these are all complexes of the NOT gate, and I don’t think we’d find them in the brain, at least not in the form of synaptic gaps, but since we are obviously capable of understanding the logic behind “X AND Y”, there’s obviously some kind of neural circuitry in the brain for processing that understanding (this incidentally is a perfect example of what I was talking about earlier: the neural circuitry in the brain is obviously far more complex than it needs to be–no AND gate per se but probably something on the order of several thousand neurons working together to figure out how to computer AND–yet the end product is a very simple and elegant algorithm, one that can be introspected and therefore implement as the AND gates we see in computers).
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
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 . . .
So don’t take anything too literally? Is that what you mean?
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.
gib
Again, another post I am not expecting a response to. Of course I am not hinting that you do not respond.
I understand the concept of lacking time for some things all too well . . .
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?! But I do think a motive is required, a desire for some kind of outcome that serves your own interests.
Motive, analogy, memes, Hmm what else - see if you can think of more - if not ask me to . . .
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.
What dictates our opinions may well form our opinions id est build biological neural networks full of opinions stored somewhere in the neocortex for more analogy and recall and refinements of our opinions. Our personal interests may change through life and offset our opinions further changing, forming and reforming.
Offset as in a consideration or amount that diminishes or balances the effect of an opposite one.
“widow’s bereavement allowance is an offset against income”
What is your opinion on that?
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.
These arguments and justifications are perhaps also complemented by emotional response to these thoughts.
Some tidbits for you gib . . .
About evolutionary and configuration emotions: if I understand it correctly, an example of an evolutionary emotion might be fear of snakes. Is this right?
One hundred percent correct gib. We can also add to this and say that we potentially evolve throughout our life; potentially not definitely.
We may be born with the neural wiring already in place to feel fear upon seeing a snake. ← We inherit that from our evolution.
There is an old study whereby children dream of local monsters early in their life even if they have never seen them before.
Grizzly bears in the USA - Lions in Africa - Crocodiles in Australia . . . the list goes on.
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?
Yes and the emotions that we choose.
And if so, what would be an example? Anger is not a chosen emotion in an Inuit culture for adults - children also grow out of it through socialization.
I can provide a few more examples if you like.
Waaay over my head.
No problem, I can make it easier - say we have a whole bunch of Coke cans and a whole bunch of Pepsi cans and only one Sprite can and two 7UP cans- now we have these cans but we only want one of each - we discard all of the cans except for one of each. Lets change our original array to look like the following:
[code]Hi gib I am output from and array of cans!
P C C
C P 7
7 C P
S C P[/code]
Let C = Coke, P = Pepsi, S = Sprite and 7 = 7UP
Because we only want one of each lets now process to get the result
. . . the result looks like the following:
[code]Hi gib I am output from an array of cans!
P ~ ~
C ~ ~
7 ~ ~
S ~ ~[/code]
Simple but significant to the brain. I only showed the functionality here - I will demonstrate further and gently at a later date.
We took away the active potential networks - the other knowns and unknowns.
We are left with unconfigured neurons - they will probably die
gib
This is possibly a little more advanced than the dialect you pointed out, however you are spot on with your observations . . .
I’m afraid this one’s over my head too, encode. But I do sense that this is at the core:
Your sense is correct . . .
abstract impression = total sum[derivativeSimilarities]/newfound
answer = analogy derived from: abstract impression
integrated answer = answer ∫ newfound
integrated answer = meaning
You said: It reminds me of the Hegelian dialect: thesis → antithesis → synthesis. The synthesis will always derive newer higher meaning. ← Is this within the ball park? To which I would reply: This is well within the ball park - I am going to look up the Hegelian dialect now, thank you gib . . .
=D>
This thread has officially outrun me. I’m going to need to stop to catch my breath.
But I will read through it eventually.
Relax gib - you more than deserve to . . .
► Please, do not concern yourself with keeping up. I am happy if you get the chance, to quickly glance at my posts.
For your information, I have also made a post in your Rationality is overrated thread.
This thread has officially outrun me. I’m going to need to stop to catch my breath.
But I will read through it eventually.
There is no need to keep up. I am happier if you get a chance to gloss over things, at your own pace. You have already responded once(and that is good enough, as far as I am concerned), and I have returned volley with a few simple responses to your post. I have not responded properly to all of your post yet, I just wanted to give you something to read in the meantime, to inspire some thought, to get you thinking - so you know, that somewhere out there - encode is thinking too.
This also gives me a chance to write a post of higher quality, about scanning the brain . . .
. . . which I think you would me more interested in reading at the moment.
I am going to be taking a relaxed approach at providing information from my point of view - there are going to be a few posts, that I suggest you just read, rather than respond to. These posts will lead up to a summary post that will be well worth your time to respond to, and I will not make the summary too long either.
Look out for my first post in the small set of posts called:
► On scanning the brain, in order to understand its ability, to process patterns of information.
We can continue our discussion after I make the summary post, I personally think it will work better that way but it is up to you.
Relax, stop and catch your breath, and read at your own pace gib . . .
Arcturus Descending
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.
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.
Thank you for the compliment 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?
I actually meant for the likes of me to understand - you might even understand it better than me I think.
What you think and do on the other hand does change the structure of the brain.
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.
That sounds like an awesome channel Arc. I like those sorts of shows and documentaries - when it comes to brain, mind and reality, I am quite passionate.
Much like you are with deep dark mysterious space.
So it’s like the scent of the rose - it gradually dissipates to barely anything?
Yeah - kinda sad in a way Arc - but I understand that brain death is a process too - I watched my poor, dear old, Great Great Auntie, die after a stroke - it took her three days before she passed - I asked her not to go, and she patted me on the head, this was while she was still lucid - when she took her last breath, it felt like I took mine. May she rest in peace.
[-o<
Relax gib -
Okay, okay, I’ll relax… geez! I will take a day at the spa.
Just didn’t want to leave you with the impression I was ignoring you.
► On scanning the brain, in order to understand its ability, to process patterns of information.
A gentle and relaxing introduction to the art and science of understanding
To scan the brain is to look at all parts of the brain carefully in order to detect it’s features.
We should first remind ourselves that scanning is not just about the machines commonly referred to as scanners - in this context we are looking at the brain with a high degree of scrutiny to reveal its architecture. We have the intention of building a sophisticated mechanism with which to understand the brains ability to empower the mind with the capacity to perform pattern matching subconsciously. Extrapolations have been previously made to help us arrive at our current understanding of the brain. Philosophically, we have been asking many questions about the brain for a long time.
Cognitive science seeks to unify neuroscience and psychology with other fields that concern themselves with the brain, such as computer science (artificial intelligence and similar fields) and philosophy. The oldest method of studying the brain is anatomical, and until the middle of the 20th century, much of the progress in neuroscience came from the development of better cell stains and better microscopes. Computational neuroscience encompasses two approaches: first, the use of computers to study the brain; second, the study of how brains perform computation.
On one hand, it is possible to write a computer program to simulate the operation of a group of neurons by making use of systems of equations that describe their electrochemical activity; such simulations are known as biologically realistic neural networks. On the other hand, it is possible to study algorithms for neural computation by simulating, or mathematically analyzing, the operations of simplified “units” that have some of the properties of neurons but abstract out much of their biological complexity. The computational functions of the brain are studied both by computer scientists and neuroscientists.
Men ought to know that from nothing else but the brain come joys, delights, laughter and sports, and sorrows, griefs, despondency, and lamentations. … And by the same organ we become mad and delirious, and fears and terrors assail us, some by night, and some by day, and dreams and untimely wanderings, and cares that are not suitable, and ignorance of present circumstances, desuetude, and unskillfulness. All these things we endure from the brain, when it is not healthy…
By using previous data, whether written or graphical in nature, we are able to enhance our exploration to uncover many of the brains still hidden secrets. We are able to make many conclusions by looking for correlations in available data against our own theories, ideas and thoughts. We are also able to create metadata that can be graphed for further visual reference(we can call these graphs, meta-graphs). The output from the machines that we refer to as scanners, and the meta-graphs that we create can be collectively referred to as scans.
Andreas Vesalius (31 December 1514 – 15 October 1564) was a 16th-century Flemish/Netherlandish anatomist, physician, and author of one of the most influential books on human anatomy, De humani corporis fabrica (On the Fabric of the Human Body). Vesalius is often referred to as the founder of modern human anatomy.
A quick scan(careful look) of these two images, reveals the basal ganglia and some history.
In vertebrates, the reward-punishment system is implemented by a specific set of brain structures, at the heart of which lie the basal ganglia, a set of interconnected areas at the base of the forebrain. There is much reward for understanding how the brain gives the subconscious the ability for pattern matching even though somtimes the effort can be rather punishing. The subconscious I believe has an intimate connection to the brain . . .
In the next part we will take a brief look at pattern matching.
[size=85]Information Source: Wikipedia.
(2017)[/size]
What I know, I take for granted. Up until now I have been trying to illustrate an answer to your question which turns out has some difficulty associated with providing an answer. Hopefully I can further improve on this for the time being. Then I can add to it later.
In the next part we will take a brief look at pattern matching but for now let us continue with this part . . .
. . . This part is going to give us a few hints. This post is no exception . . .
First let us quickly examine the neocortex. We are talking about scanning the brain and we must understand how we arrived at current day methods and why those methods are improving all of the time. Some of the methods currently not publicly available are quite sophisticated compared to those that are public.
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.
First we must understand that scanning is not just about technology - in this context we are looking at something with care to detect a feature. There is quite a bit of inference going on to say the least - to say this is done without errors is quite silly. I believe the inference is quite accurate and the computer models show this to be the case as I will demonstrate.
This will take a little time to develop, sink in and make sense.
The inference is made on the following: Cutting up the neocortex - delightful, brain scans(neuroimaging) - there are at least ten we could choose from. Interestingly the idea of neuroimaging goes back a long way and its life actually starts out in blood circulation over 120 years ago but anyway. PET and fMRI scans are very useful. EEG has added much data despite its spatial limitations - there is no substitute for cutting the brain up. Obviously microscopes(optical and electron based) give plenty of visual data.
We were able to guess at what the brain was doing before we made many attempts at breaking it down further. With the results from our thoughts we started looking for things that may have not been there but in many cases were.
Now we are able to get many high resolution photographs from microscopy.
Microscopy is the technical field of using microscopes to view objects and areas of objects that cannot be seen with the naked eye (objects that are not within the resolution range of the normal eye). There are three well-known branches of microscopy: optical, electron, and scanning probe microscopy.
However we still find use of illustrations and diagrams ever important and perhaps . . .
. . . these are more important for making guesses before developing the technology.
Illustrations, diagrams, graphs and textual data. We will first take a look at a few illustrations . . .
Using the Triune model of the Brain we come up with this illustration:
Certainly we can use other models but this will suffice for this part of our discussion . . .
The neocortex, also called the neopallium and isocortex, is the part of the mammalian brain involved in higher-order brain functions such as sensory perception, cognition, generation of motor commands, spatial reasoning and language. This illustration is where we need to start paying attention:
Here we are looking at the six layers of the neocortex that I mentioned before. The different cortical layers each contain a characteristic distribution of neuronal cell types and connections with other cortical and subcortical regions. There are direct connections between different cortical areas and indirect connections via the thalamus, for example. The thalamus has multiple functions. It may be thought of as a kind of hub of information.[clarification needed] It is generally believed to act as a relay between different subcortical areas and the cerebral cortex. The cerebral cortex can be classified into two parts, the large area of neocortex and the much smaller area of allocortex - we are examining the neocortex.
Thanks to Wikipedia for most of the information here . . .