On Computing the Brain and Mind

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.

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 . . .

Motive, analogy, memes, Hmm what else - see if you can think of more - if not ask me to . . .

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?

These arguments and justifications are perhaps also complemented by emotional response to these thoughts.

:-k

Some tidbits for you gib . . .

One hundred percent correct gib. We can also add to this and say that we potentially evolve throughout our life; potentially not definitely.

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.

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.

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

  • we know we only keep the first column which just so happens is a linear network in the array . . .

. . . 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 . . .

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.

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 . . .

:wink:

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.

Thank you for the compliment :smiley: I will dig up what I am talking about and post it here - it is quite simple to understand.

I actually meant for the likes of me to understand - you might even understand it better than me I think. :smiley:

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

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.

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<

Okay, okay, I’ll relax… geez! :laughing: 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

  • pattern matching as it pertains to the human brain and mind.

:diamonds: 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.

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.

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.

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 . . .

Next comes what I like to call the grid - we are looking at the columns from the top down:

This illustration is convenient because we get to see the columns as well as the “grid”.

Here we are looking at abstract connections between the thalamus and the multiple planar(grid) layers of the neocortex. In case you are wondering whether there is a missing dimension in my post - fear not - it is a trick of the mind - I can explain further.

We have three new dimensions(grids(planar layers), columns) to work on top of the regular three dimensions(x,y,z) plus one of time(vicinity) making seven dimensions. Let us make more sense of these dimensions as follows:

  1. Spatial Dimension - X
  2. Spatial Dimension - Y
  3. Spatial Dimension - Z
  4. Grids
  5. Columns
  6. Layers
  7. Times

By using the spatial dimensions and time we can create 3D pictures of a pattern at any given moment. We can then infer what the grids, columns and layers are doing. This all works in reverse and is just simplified for the sake of our discussion. Each of the layers have two distinct dimensions.

The previous explanations are somewhat simplified, and I may have even added an extra dimension - but we can improve on accuracy here.

We can simplify the neural connections for a computer model by adding extra planar layers - each containing two dimensions and then we stack them to make a third dimension, finishing with time as our reference point and a fourth dimension. Each planar layer becomes an analogy of sorts.


In the middle are the planar layers and to the right is a representation that one atom on the grid is a neuron.

This image is from a company called Numenta who are quite advanced with their research.

The thing to note is the stack of planar layers.

This is a different way of looking at the same thing. Here we are using analogies between layers as I will demonstrate in the next illustration.


Notice that Cat and Dog are the most similar followed by Cat and Fish for what ever reason.

Take note that the neocortex is looking for similarities between events - these are analogies and vicinities as previously discussed. Side on we can start looking at it a different way - as is illustrated in the following image:


This is a side view - we are looking at some stacked layers side on. Making connections that can be imaged.

This is where the multidimensional network starts to produce patterns - not yet optimized but yet sufficient to understanding.

Now we can start some imaging - false color type in the next image:

With all of this in mind we can go further to create spatial patterns that can be compared to each other - thereby giving all the x,y,z,t connections that are taking place - on a live scanner one would notice that thoughts produce distinct 3D patterns > that can be superimposed onto brain scans - some of which will be 3D.

From here we move on to develop theories of how to make all of this happen - we return to inference which is of course a conclusion reached on the basis of evidence and reasoning. Using the current scanning techniques along side our theories we can narrow down the field of information we are looking for.

Each time we narrow down the field of accuracy in the information we can inversely increase the resolution of scans.

► Computer Science 101 - My Way


A brief tour of stuff from computer science related to the brain, mind and cognition.

Forward
I want to point out that imagination has to be used alongside scanning to make leaps of understanding how the brain and mind process patterns in nature. As can be inferred from what we have already covered, these days there is an intimate connection between people and technology when it comes to understanding the brain and mind. Following are brief introductions into Pattern Matching, Pattern Recognition, Statistical Inference, Computer Vision, Speech Recognition(Hearing) and Hierarchical Temporal Memory. What we will do with these introductions is to mash them together into an insightful theory of how they are related to the brains ability to endow the mind with the capacity to recognize patterns by virtue of analogy and vicinity.

Pattern Matching
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact. The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace).

Pattern Recognition
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled “training” data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).

Statistical Inference
Statistical inference is the process of deducing properties of an underlying probability distribution by analysis of data. Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be sampled from a larger population.

Vision
Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

Hearing
Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or just “speech to text” (STT). It incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields. Some speech recognition systems require “training” (also called “enrollment”) where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person’s specific voice and uses it to fine-tune the recognition of that person’s speech, resulting in increased accuracy. Systems that do not use training are called “speaker independent” systems. Systems that use training are called “speaker dependent”.

Hierarchical Temporal Memory
Hierarchical temporal memory (HTM) is a biologically constrained theory of machine intelligence originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee. HTM is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the human brain. At the core of HTM are learning algorithms that can store, learn, infer and recall high-order sequences. Unlike most other machine learning methods, HTM learns time-based patterns in unlabeled data on a continuous basis. HTM is robust to noise and high capacity, meaning that it can learn multiple patterns simultaneously. When applied to computers, HTM is well suited for prediction, anomaly detection, classification and ultimately sensorimotor applications

Keep in mind that all inputs are encoded for the machine and all human inputs are also encoded.

It is this encoding that shows me the mind is indeed much different to the brain . . .
. . . and the minds thoughts must be translated many times for the brain . . .
. . . to process - but neither the mind or brain understand each other . . .

Pattern recognition in the brain is less tolerant than the minds abilities - much like hardware versus software.

Bringing it all together - this is what we are about to do . . .

Now gib

I am wondering how far we may have stretched your imagination here.

I am hoping you can see that the ball/wall system is much different to the brain compared to the mind if you have not already seen that before I came along - that is going to make it easier. The sensation, that is, mind and brain are the same, is fallacious.

This sensation that I have mentioned is not well thought out in some science while slightly less than half have an idea of what I am speaking of.

Not many consider the connection you and I have made between computers and brain - software and mind - et cetera . . .

:-k

Let us begin to make the connections between these patterns - perhaps you will know before the scanner comes into being

  • that the scanner must first learn its patient before it can scan accurately - not the chicken or egg.

Arc

You know, that is a great description and it highlights how the mind is more tolerant than the brain when it begins to mold itself around such description. Emotion arises within because of a rational mismatch - this mismatch exerts chemical influence back into the body - what goes up must come down - every time.

Without such release the brain can never win as much as with only such release.

:wink:

It reminds me of the mood pond being influenced by emotional ripples . . .
. . . eventually coming home to the whole when the waves reach the shores . . .

To be continued . . .

Patterns are bound to belief.

Patterns are bound to rationality.

Patterns are bound to emotion.

Patterns are bound to reality.

Mind is a pattern . . .