From the Neuron to Human Intelligence: Part 1: The “Ideal Brain” Model

From the Neuron to Human Intelligence:
Part 1: The “Ideal Brain” Model
Christopher F. Reynolds
Tring, Herts, HP23 4HG, UK

This discussion paper asks what are the minimal properties of an animal brain that can remember and recognise patterns and use the information to make simple decisions. It then shows how this could be modelled in an ideal brain (with analogies to the ideal gas of physical sciences) consisting of recursively interlinked identical neurons. It then shows how such a simple brain could carry out significant information processing by comparing it with earlier research into the possibility of building an intrinsically human-friendly “computer”. The model reflects a number of features of the human brain, such a confirmation bias, but it is also clear that as long it could not support the human level of mental activity on trial and error learning within a reasonable time frame
Part 2: Evolution and Learning (to be posted here shortly) will look at human evolution and suggest possible evolutionary routes for an ideal brain which lead to a tipping point in learning mechanisms which made a cultural explosion possible.
Table of Contents
1 Introduction 2
2 Modelling the Ideal Brain 2
2.1 Modelling the Connections – The Basic Mechanism. 2
2.2 Modelling the Meaning – The Unknown Dictionary Model 3
2.3 Defining the Memode 3
3 Some simple examples 4
3.1 Macbeth 4
3.2 The Peacock 5
3.3 I am Hungry 5
4 CODIL and the Brain Model. 6
4.1 The Background to CODIL 6
4.2 How CODIL differs from the Stored Computer Model 6
4.3 The parallel between CODIL and Memodes 7
4.3.1 CODIL Items 7
4.3.2 CODIL Facts and the Brain's Working Memory 8
4.3.3 Decision Making and Learning. 9
4.3.4 Other CODIL Features 9
5 Discussion 10
5.1 The Ideal Brain is not a Real Brain 10
5.2 The Problem of Learning 10
5.3 Formal Logic and Confirmation Bias 11
5.4 Memory and Uncertainty 11
5.5 A Note on Nomenclature 11
6 Conclusion 12

1 Introduction

While there is a very considerable amount of research being carried out, in many different disciplines, relating to the human brain, how we learn, and how intelligence has evolved, there appears to be no agreed neural code model to bind the research together. The aim of the research described here is to propose a possible draft framework which starts with the electrical activities at the neuron level and provide a plausible evolutionary path to explain the human intelligence phenomena. This paper describes an “ideal brain” model which suggests how information could be stored and processed in an simple animal brain which can recognise and remember patterns, and use those patterns to make simple decisions. Part 2 will look at how such an ideal brain model could evolve to support what we now recognise as human intelligence.
In describing this model it is important to realise that we see the world in terms of what might be best described as an ocean of cultural intelligence and that when this is stripped away to reveal the genetically determined brain, as described here, a number of the features appear which many will find counter-intuitive. It is also important to realise that this model is intended to provide a framework for planning future research, and the aim is promote discussion on how the many different approaches to brain research might be brought together in a unified model of human intelligence.

2 Modelling the Ideal Brain

Any animal which can assess its environment and react appropriately must have a brain that can learn patterns and use those patterns to recognise objects in the environment. It should be able to use such patterns to make decisions as to what actions need to be taken by the animal. In addition it must work in a brain which consists of network of interlinked neurons. The aim here is to construct a model which will have these properties, is capable of non-trivial information processing, and which could have evolved from simpler neuron networks. Of course it is only an idealised model and the interesting part is to find out how, and why, real brains differ from the model.

2.1 Modelling the Connections – The Basic Mechanism.

Think for a moment about the model of an ideal gas in the physical sciences. All molecules are deemed to be identical. They are moving about at varying speeds in various and colliding with each other. They are considered to be in an infinite container – so that one can forget about the problems of colliding with the walls of the container. Of course real gases deviate from this model, in various ways but one of the values of the model is that it allows such deviations to be identified and understood.

Now think about an “ideal brain“. It contains and infinite number of identical neurons. Neurons exchange signals via pathways of varying carrying strengths. (The exact form of the signal is not part of the model – but the important factor is that some pathways are more effective than others.) . The strength of the pathway depends on the amount of traffic along it, and if there is no traffic the strength will drop to zero. The model assumes every neuron is linked to every other neuron but in most cases there is no real link as the strength of the connection is zero, which is the default start value. This ensures that initially the brain is empty of information and connections are only established once the brain is working and has learnt something.

Between any pair of neurons signals may pass in either direction but by different mechanisms which will be referred to as “up” and “down” signals . The strengths of the connection need not be the same in both directions, and in some cases the connection may only be one way.

A neuron normally becomes active if the strength of all up signals it receives from neurons “below” it exceeds a threshold – and once active it sends up signals to the neurons “above” it. A neuron which does not have an up link strong enough to pass on a message will be referred to as a “top” neuron. “down” signals pass in the opposite direction and will be discussed later.

In the initially empty brain some of the neurons will have inputs from outside (in a real brain from sense organs) and when they get a signal they will initially be “top” neurons. Learning takes place when there are several “top” neurons simultaneously firing. If neurons A, B, and C are firing they will all make links with another neuron, let us say M and from then on when A, B, and C fire they will fire to alert neuron M – which becomes the “top” neuron. Similarly D, E, and F firing together alert N as a top neuron. Now if M and N are active “top” neurons at the same time they might make links to another neuron Z – which now becomes the “top” neuron when M and N are firing. This process is recursive and in the ideal brain there is no maximum to the depth of nesting of linked neurons.

The important thing to realise is that this is a learning process, in which the strength of the links between neurons can increase with use, or fade away with neglect and the brain will fill up with networks leading to higher and yet higher level “top” neurons with time. As will be seen later the time allocated to learning, the speed of learning, and the depth of nesting of levels are critical to understanding the evolution of intelligence.

2.2 Modelling the Meaning – The Unknown Dictionary Model

Imagine you have come across an alien dictionary, written in an unknown language. Each page has a word written at the top, and underneath there are definitions of that word, written in the same language. You don't know what the word means – but you can use its definition to work it out – except that all the definitions are written in the same language – and eventually you find yourself going round in circles getting nowhere. Without any pictures or other guidelines you don't know what any of the words mean. There may be useful information stored in the dictionary but you don't have the key.
In fact the words at the top of each page are, to you, simply arbitrary symbols, and you could simply replace each word with the page number on which its definition occurs, without destroying any of the meaning. Taking this one stage further imagine each page to be replaced by a post and run a string from each post to all the other posts which are referred to in the definition. Having done this you take the posts away – leaving knots where the posts were – and end up with a very tangled ball of interconnected wires. It may look very untidy – but all the original links are still there.
So let us take this dictionary model and map it onto the ideal brain model defined above. Each knot becomes a neuron and its definition is in terms of “down” connections to lower level neurons – and the whole process is recursive – going on to lower and lower levels. In addition most neurons will have up connections with neurons which represent higher (and recursively ever higher) concepts.

2.3 Defining the Memode

In discussing the behaviour of the ideal brain and comparing it with actual brains we need a handle to address the contents of this amorphous network and I am calling this handle a memode.

Every memode consists of a single “top” neuron, together with all the “lower” neurons which can send it “up” signals and/or can receive its “down” signals.

The following points apply:
  • The definition is recursive and the memode reaches down through an unspecified number of levels.
  • Every top neuron can also be a lower neuron in a possibly large number of other memodes.
  • A memode is active when the top neuron is active. In addition the lower neurons in the memode which triggered the activity will be active – but neurons in the memoded not involved in triggering the activity will remain passive.
  • There is no limit on the depth of nesting of memodes or the complexity of the concepts a memode can represent. However there is no difference between neurons which represent a lower level concept and a high level concept as all neurons are identical.
  • The definition is useful in discussing human thought and language – as it helps to relate each human concept to a particular memode.
  • The meaning depends on the leaning history of the links between the lower memodes and will dynamically change with time.
  • The memode is no more than a network of connections buried in a network of connections representing other memodes and there is no simple answer to the question as to where exactly information is stored. An active concept is just the sub-set of neurons which are active in a particular memode.

At this stage it is appropriate to suggest the following links between the ideal brain model and the human brain:
  • The human working memory consists of the memodes that are currently active.
  • At any one time there is a limit to the numbers of discrete memodes that can be active. (Compare Millers Magic Number Seven)
  • Our conscious thoughts represent the concepts associated with the currently active memodes – including the active lower neurons in the memodes.

3 Some simple examples

3.1 Macbeth

Imagine you are watching Shakespeare's play Macbeth, which you have never seen before. Your brain has already identified the new concepts “Macbeth” and “Duncan” and as Act II, Scene I, approaches it end a existing concept “dagger” becomes active. Then comes the lines
I go, and it is done; the bell invites me.
Hear it not, Duncan; for it is a knell
That summons thee to heaven or to hell.

Another concept flashes into your mind “murder” and the brain has active memodes for “Macbeth”, “Duncan”, “dagger” and “murder.” The brain remembers this event by creating a new, higher, memode above the currently active memodes being the “down.”
For discussion purposes in these posts the resulting memode will be related as
Death-of-DUNCAN {Macbeth, Duncan, Dagger, Murder}
Of course these words are not directly stored in the “top” neuron – they are simple used as labels for the purpose of this discussion – and a mouse brain may well have a memode which corresponds to
Cat {Sight-of-cat, Sound-of-cat, Smell-of-cat}
while the part of the brain concerned with hearing would have memodes linking phonemes, with similar memodes for sight and other senses. Because all neurons and links are the same in the ideal brain the model uses the same learning model at all levels and all inputs.

3.2 The Peacock

What kinds of information will a human brain hold about a peacock? One possibility would be
Peacock {Sight-of-peacock, Sound-of-peacock, Word-for-peacock, ...}
Sight-of-peacock {Looks-like-bird; Size-of-goose; Peacock-tail, ...}
Peacock-tail {Looks-like-fan, Looks-like-eye, ...}
Word-for-peacock {Sound-of-word-peacock, written-word-peacock}
When someone sees a peacock a signal will pass up (starting with the neurons connected to the eyes) and leave a trail of active neurons stopping at the top neuron in the Peacock memode. The neurons involved will vary depending on what the eyes can see – for instance if the tail is folded down the Looks-like-eye memode branch within the Peacock memode would not be activated.
If the activated Peacock memode is of lower priority to other simultaneously active memodes in the brain's working memory, the activity in the memode will decay and it will drop out of the working memory. Different parts of the Peacock memode would be activated if the human ears heard Sound-of-word-peacock and this time some down links may be activated – perhaps via Sight-of-Peacock, Peacock-tail, and Looks-like-eye – and the listener imagines he can see a peacock's tail. The important thing to realise is that exactly the same neurons are used to recognise an object and to remember it – recognition is through what I call the “up” links, and recognition through the “down” links. This also suggests that dreaming may simply involve visiting the down links, exploring sound, smell, or sight images from the memory.
On another occasion the human might be writing a letter and a sudden shriek from the garden activates the Peacock memode – and down signals are sent to the Word-for-peacock and this is passed on to Written-word and beyond – and the human writes the word “peacock”. This involves the use of the down links to trigger activity.

3.3 I am Hungry

Now let us consider the type of memode that is likely to be present in all animal brains in one form or another.
Want-to-eat {Hungry, Food, Eat}
Hungry represent a memode which becomes active if sufficient lower memodes send a combined “up” signal, and when Hungry is active Want-to-eat automatically becomes active, but initially Food and Eat are not active. So Want-to-eat sends a “down” signal to Food.
Food {Orange {...}, Apple {...}, Banana {Sight-of-banana, Taste-of-banana}}
Effectively what has happened is that the top neuron of the Food memode has been asked if any of its lower memodes are active – and if for example the Banana memode is active, the Food memode becomes active and Want-to-Eat sends a down signal to the Eat memode – which triggers the act of eating.
However if Banana memode of not active it would send a down signal to the even lower Sight-of-banana memode – which might trigger the neurons responsible for eye to look around – and if the result is still negative trigger the host animal to go and look for the banana palm.

4 CODIL and the Brain Model.

If the “ideal brain” model is going to be useful in explaining human intelligence we need to understand how good it is at actually processing information, and what its limitations are. At a first glance it looks very simple and as it was only intended to be a minimal model capable of doing some useful work in an animal's brain it is very easy to jump to the assumption that it can only learn simple patterns and make simple decisions. Fortunately there is a model available, with experimental data, in the shape of an abandoned blue sky computer project. The CODIL project was exploring whether it was possible to design an information processor which was human-friendly at the central processor level. It is shown below that the system's language (CODIL) can be mapped onto the “ideal brain” model, and while CODIL could do (and was deliberately designed to do) some things which the one would not expect of the “ideal brain” its is clear that there is very considerable potential to handle some classes of complex problems.

4.1 The Background to CODIL

CODIL (COntext Dependent Information Language) was the symbolic assembly language of a hypothetical non-Von Neumann information processor which it was hoped would provided a human-friendly alternative to the inherently human unfriendly inner workings of the stored program computer. It arose from a design study carried out in 1966-67 into the sales accounting systems of a very large oil marketing company, Shell Mex and BP, when they were looking at ways of transferring their magnetic tape batch processing system (which handled about 250,000 customers and about 5,000 products) onto a system with online access. The study suggested that, rather than try to pre-defined every possible requirement in advance, it might be possible to build a system where sales staff and the computer worked together using a common two-way language – so that the computer could always explain to the sales staff what it was doing and why.
This approach was considered too revolutionary at the time but later in 1967 it was realised that the design could be generalised and, with support from the UK computer pioneers John Pinkerton and David Caminer, a small research team was set up within English Electric Leo Marconi and patents relating to the processor of a radically different kind of computer were taken out. The pilot program set up to look into the approach met all its targets (The CODIL Language and its interpreter, Computer Journal, 1971) but unfortunately the project was closed down when the Research Department was closed when the company International Computers Limited was created. The research continued on an unfunded basis at Brunel University for some years – and CODIL was shown to be able to support a wide range of applications including a very powerful Artificial Intelligence type problem solver called TANTALIZE. A logically powerful subset, MicroCODIL, was trial marketed as a schools teaching package on the tiny (32K byte) BBC Microcomputer (to demonstrate that the approach did not require the a high powered machine to implement)and received many favourable reviews but the the project was effectively abandoned (in part for medically related reasons after a family suicide) two years before a detailed definitive paper appeared in print (CODIL – The Architecture of an Information Language, Computer Journal, 1990)

4.2 How CODIL differs from the Stored Computer Model

The stored computer approach in many ways reflects the scientist's aim in doing research. The goal is to create an algorithm or global theory which will process/explain potentially very large collections of formalised numerical data. This approach is particularly appropriate to problems involving lengthy and/or sophisticated applications which the average human brain would find impossibly difficult. The approach will only handle a given task if someone (presumably human) has created the algorithm/theory and thus represents a top down approach to automatically processing information.
Because the stored program computer originated performing numerical calculations processing array of numbers (which can be addressed by number) it is not surprising that at the hardware (or microcoded processor) level it handles words containing numbers. These numbers may represent actual numbers (in various formats), text relevant to the task, addresses (including addresses of addresses and address modifiers), program symbolic names, etc. The meanings any given word depends on both context and history and this is one of the reasons why computers are “Black boxes” where the user cannot look inside (at the hardware level) and see what it is doing in understandable human terms.
CODIL takes a diametrically opposite approach, working from the bottom up and will handle open ended tasks, potentially involving incomplete or uncertain information, where it is not practical (or in some cases possible) to pre-define a global model. It is particularly appropriate where humans and “computers” have to work together with uncertain and ever changing real world tasks. Instead of numbers CODIL uses symbols where every symbol is the name a real world object or of a set or a representation of a subset. In addition any symbol may represent a list of symbols which provide an expanded definition. Processing is by a remarkably simple but highly recursive decision making unit which simply scans the systems memory comparing symbols/sets and treating each symbol it finds as either “passive data”, a condition (there is no explicit “IF” in the CODIL language), or to modify the current context. Apart from a few pre-defined symbols to control things such as the input and output of information there are no explicit commands in the CODIL language. The whole idea is to have a WSYIWYG type system where everything the system does uses the human user's own symbols and these are manipulated in such a way that the process is obvious to the human user.
Of course CODIL approach would become impossibly cumbersome and very slow if it was used implement formal mathematical tasks involving sophisticated mathematical operations of large arrays of well-structured data – because it has no automatic way of addressing data held in numerically defined storage locations. However the important thing to realise is that CODIL was optimised to work symbiotically with humans on open-ended tasks and complements, rather than replaces the way that stored program computers are usually used.

4.3 The parallel between CODIL and Memodes

4.3.1 CODIL Items

CODIL was designed as toolbox to allow people to process information with a computer-like box and the system therefore contains features relating to the hardware (for instance interacting with a keyboard) and such features are clearly irrelevant to modelling the brain. In addition it contained a number of “advanced” features – such as handling numbers and word processing – which are clearly irrelevant to a simple brain – and again these will be ignored in the following discussion.
The basic unit of information in CODIL is an item, which can be a single word, or a word pair, and these are arranged in lists of lists. This means that
Macbeth; Duncan; Dagger; Murder.
is a valid list of CODIL items, and this should be compared to the memode example earlier.
Death-of-DUNCAN {Macbeth, Duncan, Dagger, Murder}
In CODIL Death-of-DUNCAN would be the name of a file containing Macbeth, Duncan, Dagger and Murder - but as all filenames are automatically item names we have a recursive structure where any item can be a list of items).
So in memode terms each CODIL item is a symbolic name identifying a memode and a memode contains a list of memodes, each of which are equivalent to CODIL items.
But CODIL items can also be word pairs such as
Murderer = Macbeth; Victim = Duncan; Weapon = Dagger; Action = Murder.
At the human level this is far more meaningful as, although it is simply a list of item pairs, and contains no verbs, it can easily be related to natural language statements, which is why most CODIL application use item pairs – and why most CODIL files are immediately meaningful to human users.
This can also be interpreted in memode terms with Murderer being the symbolic name of a lower level memode in the memode with the symbolic name Macbeth.
CODIL can also have items such as
Murderer IS = Person
which in memode terms indicates that any memode which contains Murderer must also contain Person.

4.3.2 CODIL Facts and the Brain's Working Memory

At the heart of the CODIL interpreter are a series of registers, each of which contain an item, which describe the current context. The facts may be generated from new input, by loading in remembered contexts – so one can remember items that had been there earlier, and by making deductions (discussed later). In addition the context defined by the current Facts can be saved as part the systems knowledge base of CODIL statements.
In mapping CODIL onto the memode model the Facts are therefore symbols for the memodes which, at any one time, are active and form part of the brain's conscious working memory. The process of saving the contents of the Facts is the equivalent to creating a new “top” memode. In addition loading a CODIL file such as Death-of-Duncan into the Facts will restore the values to the list of items Macbeth; Duncan; Dagger; Murder.
This is important in the brain model as allow the brain to remember past contexts from the top down, rather than bottom up from current inputs to the brain. This means that under some circumstances, when a memode becomes active a “down” signal can activate the level of memodes immediately below the “top” neuron. Such a mechanism is essential id the brain to to be able to selectively remember the past and use its recollection to guide its actions.
A key question is the relationship between the number of CODIL Facts and the size of the brain's working memory. At a very early stage of the research into CODIL is was realised that if the system was to process information is a way that the human user could understand the number of the Facts registers had to very small – especially when compared with the number of symbolic names that would be needed in a conventional computer program of the same size. This was partly because in CODIL the symbolic names represent the meaning of the contents, and not the stored program computer address, and because it turned out that the Facts rarely needed to contain much more than half a dozen active items.
There is an immediate and obvious difference between the default way in which CODIL controls the number of active Facts and how the brain works. The reason is that CODIL was designed to be a practical tool and the first requirement is that the user is in control and the system must never try to “trial and error” learn unless the user specifically wants this to happen. The result is that CODIL has what can be considered garbage collection procedures to ensure that Facts items are automatically removed when they are no longer needed. Perhaps the brain has something equivalent but it is more likely that the activity of memodes in the working memory simply reduces with time, and the least used ones simply drop out of the working memory, in some cases being replaced by more active new memodes.

4.3.3 Decision Making and Learning.

The CODIL decision making routine (once you ignore input and output) does little more than compare statements from the knowledge base, item by item, with the items in the Facts, and if it deduces an item is true the item is moved to the Facts. This is equivalent to following links between memodes and deducing that an inactive memode needs.
For instance the Facts could contain
Murderer = Macbeth; Victim = Duncan; Weapon = Dagger; Author = Shakespeare.
and a statement in the knowledge base contains
Murderer = Macbeth; Victim = Duncan; Action = Murder.
So the items on the left are compared and the item Action = Murder (on the right) is moved to the Facts.
Murderer = Macbeth; Victim = Duncan; Weapon = Dagger;
Author = Shakespeare; Action = Murder.

However it is important to realise that in CODIL the human user describes information in terms of items and the way they are linked so one can easily deduce that if the system knows about Macbeth and Duncan it can deduce the fact of a murder. On the other hand if there has been a murder the system should not automatically deduce that Macbeth did it.
This suggests that as soon as you try to apply the model to a real brain there is a potential difficulty, as you introduce another level of learning, as the brain not only needs to build a network of memodes by some form of trial and error learning (building the up links) , but it also has to learn what deductions it can validly make between memodes (building the down links). If learning involves a simple trial and error type the amount of time spent learning get will increase rapidly as the number of memodes increases – and could well be a limiting factor in the maximum size of a real brain.
A study of some of the CODIL applications highlights an even bigger learning problem. The brain model says nothing about the order that things are done in and this raises the question of serial and parallel processing when applied to more complex tasks. The position is best illustrated by the most complex test application implemented in CODIL.
TANTALIZE was a problem solving package which could find the answer to a wide range of logic problems and actually solved 15 consecutive New Scientist Tantalizers (now called Enigma) in a row. It illustrates the fact that while CODIL makes no distinction between “program” and “data” the user can actually use the language to write “programs” - with files of CODIL statements being used as production rules. The package worked in three stages. The first asked the user to describe the problem. The second took the description and converted it to a set of production rules,and the third stage was simply to obey the generated production rules. In many cases a learning function built into the CODIL interpreter was used to optimise the order in which the production rules were executed as this often significantly reduced the size of the problem space to be searched.
While I would not claim that it is possible to map TANTALIZE onto the ideal brain model there is sufficient to believe that the success of the package demonstrates that the ideal brain model is capable of supporting a range of logically complex information processing problems. However it also highlights the problem that the CODIL system was told how to do it – and the complexity of the solver is such that it could not be learnt by trial and error.

4.3.4 Other CODIL Features

CODIL contains a number of features which were included to make the system usable for a wide range of task appropriate to an information processor in today's world, but would be irrelevant to a simple animal brain model, and so were not mentioned earlier. In some cases the features described below were handled differently in different version of the CODIL interpreter.
Handling Numbers: The value part of a CODIL item – as in “Price = 49.95” - could be a number and arithmetic expressions were possible and in MircoCODIL the full range of facilities in the BBC Micro Computer Basic were available. Because any CODIL item represented a set or a subset items such as “Born > 1975” or ranges could be handled routinely, making it easier to handle less well defined information.
Lists: Lists of members of a set were possible, as in “Colour = Red & White & Blue.” Normally one would only be interested if the list was true or false, but for some kinds of task a simple indexing facility allowed one to distinguish between different items in the list. Negation was also possible as in “Disease NOT Measles”. (The relevance of negation is discussed later.)
Inexact Matching: MicroCODIL included a series of fuzzy/approximate matching facilities which replaced the True/False decisions with a variable probability threshold. While the techniques used are not directly appropriate to the ideal brain model they at least indicate the approach will work in circumstances where exact matching is inappropriate.
Learning Functions: A limited number of experiments were carried out where the number of times items in the Facts (the equivalent to the ideal brain's working memory) were accessed were recorded, and used to garbage collect items. In addition a working area of the knowledge bases could be run with similar access records – with the most frequently used information rising to the front and the least used (in some cases) being dropped from the back. This was used to great effect in the TANTALIZE package – which used the learning facility to optimise the order by which rules were used.

5 Discussion

This model raises many questions – and it is not practical to examine them all here. So I have chosen some I feel are important, and will address others on the blog – the selection depending on which issues are raised by readers of this discussion paper

5.1 The Ideal Brain is not a Real Brain

There are obvious ways in which a real brain differs from the ideal brain – and the most obvious is that different parts of the brain serve different functions. Because there are well defined connections between the different areas, and as they clearly exchange signals, they almost certainly use the same “neural code”. In evolutionary terms one would expect changes which optimised the ideal brain model (and perhaps supplemented it) in a way which optimised the particular activity being carried out, and one would expect the biggest differences in the part of the brain dealing with critical senses such as sight. It is not the purpose of this paper to speculate on such differences – but rather to concentrate on the generic parts of the brain relating to memory and decision making – the frontal lobes in human beings.

5.2 The Problem of Learning

The ideal brain model is based on the assumption that it starts with no effective connections between neurons, and learns to recognise more and more complex patterns.. If the amount of learning is small and the decisions take remain simple simple trial and error learning would be quite adequate for an unsophisticated animal brain. But in the real world learning takes time and so an animal with a larger brain needs to take more time (longer before maturity) to be loaded with the information it needs to survive. As the brain handles more and more complex situations the trial and error learning time starts to increase rapidly as there one is now concerned with not only learning individual concepts – but also the relationship between them. There is another learning hurdle when considering what is needed to make tools on – when a network which processes information in parallel has to learn a series of activities which only work if all activities are learnt correctly and in the right order!
I have not looked at a mathematical model to see how trial and error learning times would increase with the size of the brain and the complexity of the task – but it is clear that the human brain couls not function on trial and error learning alone.
Rather than discuss the issue further here I have written a companion discussion paper “Fromthe Neuron to Human Intelligence: Part II: Evolution and Learning.” This looks at a possible pathway, concentrating on the last 5-10 million years of human evolution, which identifies some key tipping points that would explain the sudden emergence of a much more intelligent being.

5.3 Formal Logic and Confirmation Bias

Any mathematician looking at the ideal brain model will have notices a serious “flaw” which means that it is not even a good model of primary school set theory! As described the model cannot handle negation. This is quite deliberate as the aim is to start with a minimal animal brain. A mouse does not need to learn “IF food AND NO cat THEN eat” because the simpler high priority “IF cat THEN run away” would rapidly take the mouse away from the food before “IF food THEN eat”.
So at the animal level the feeble logic capability of the ideal brain is not an obstacle – but it has interesting effects when we consider human psychology. Learning involves matching input patterns with remembered patterns and making decisions as a result. But if something is not present it does not generate an input pattern.
This means that the ideal brain model will, in the words of the Harold Arlen song “Accentuate the Positive – Eliminate the Negative – Latch onto the Affirmative.” In fact the model predictes the well known feature of human brains – confirmation bias.

5.4 Memory and Uncertainty

An animal brain evolved to allow information from the past to influence the animal's behaviour in the present – and not to reminisce. When a memode in the ideal brain is activated for any reason only parts of it will be activated, and sometimes new links will be made. Useful information is reinforced when it is used while unused information fades away and is eventually lost so over time the memode will change. If you reminisce the relevant memories in an ideal brain they will automatically change because they have been activated, and the more someone thinks about the past the more their memories will change. This makes leaning the truth in a court difficult when many years separate the event from the trila, and some of the wintesses have been reliving traumatic memories.
It may well be (with an analogy to the ideal gas model) that the Uncertainty Principle applies to the brain – because in trying to record the information in the brain the information changes.

5.5 A Note on Nomenclature

In terms of other research it is important to point out that in the ideal brain model all neurons are identical and each is the top of a memode which is recursively defined as the sum of the definitions associated with all the lower memodes which can send it signals. As memodes overlap and the properties of links between neurons will change as the brain learns, this this a deliberately fuzzy definition.

In established research several terms are used to identify”different” types of neuron. Grandmother cells which react to specific complex external objects and only to those objects and are equivalent to the top neuron in a memode. But in the model all neurons are equivalent and represent some external object of abstract concept – all neurons are grandmother neurons unless some arbitrary measure of complexity is applied. The same comments apply to the related idea of Concept Cells.

In the same way every neuron in the ideal brain model has the ability to work as a Mirror Neuron but in many cases there will never be any call to act in this way – and in other cases it would be difficult to carry out experiments to observe the behaviour. While this means that, in the ideal brain model, there is no special kind of mirror neuron, the concept is useful when it come to discussing Evolution and Learning in Part 2.

6 Conclusion

There is no formal conclusion because the ideas are put up here for open discussion and debate. All I have done is to note that many people seem to have been having difficulty in finding some way of relating neurons to human intelligence. I thought a project I worked on over 20 years ago might be relevant and mapped my finding on that project into the ideal brain model.

Having done this I feel that could form a good starting point for debate on how neuron activity relates to human intelligence,  but as a scientist I know that ideas that look promising can lead to dead ends. Even if it turns out that the model is flawed – discussing its strengths and limitations could help others to come up with better models.
So if you like it say why
If you disagree your comments are even more valuable – as the key to good research is to discover the awkward questions.
If you are not sure whether it is relevant to your research – tell me about your research so I can suggest how it might fit in.
And if you have any constructive ideas for exploring the concepts further – your contribution is very welcome. 

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