OpenLLM-France/wikimedia huggingface dataset:
# Artificial Consciousness/Neural Correlates/Neural Groups/Laberge's Apical Dendrite Function
## LaBerge - Kasevich Theory of the Apical Dendrite

So far we have encountered theoretical functions for the first 4 layers of the cortex, and the 6th layer, so to round out the model we need a theoretical function for the 5th layer. While there might be other models out there, the only one I know of, is The LaBerge - Kasevich theory of the Apical Dendrite, which links the 5th layer of the cerebral cortex to the thalamus, via the Apical Dendrites of the 5th layer pyramidal neurons.
LaBerge and Kasevich's work is on the Attention System, and so their theory is based on the idea that the 5th layer is the target of the bottom-up attention system. while I disagree with some of the assumptions they have made, mostly about the nature of Awareness and Consciousness, I think their work is important, if only because it stresses the importance of the length of the Apical Dendrites of the 5th layer pyramidal neurons, to the timing of attention, and links the bottom-up attention system to the Mini-column architecture of the Cerebral Cortex.
The idea is that 80 to a hundred of pyramidal neurons from layer 2/3 form mini-columns, and the 5th layer Pyramidal Neurons act as the axis of those mini-columns. Then about a hundred of the Mini-Columns form a column as described in the center surround function page.
Thalamic connections cause the Axis Pyramidal Neuron to fire, and this in turn causes the layer 2/3 pyramids to pre-activate which encourages them to respond to any patterns found in the stimuli coming from the mossy dendrite layer. This might in fact be necessary for the stimulation of the phenomenally implicit memory using a place-code addressing scheme.
Now this might seem confusing, since it suggests an activation role for the 5th layer, that connects at a level below the Column Architecture, and so causes us to ask, at what level does the brain react?
However we can clearly see that at one level of organization the output is likely to be at the mini-column level, and at another level of organization the output is likely to be at the column level. Some experiments have shown that the center surround effect is less prevalent on the Right Hemisphere which would make the output at the minicolumn level dominate, while the left hemisphere has a stronger center surround function and so the column level is more likely to dominate.
# Artificial Consciousness/Neural Correlates/Neural Models

Neural Models
When Hebb first proposed a Mathematical Model of the Neuron, he had a problem, it wasn't practical to study human neurons, so he was forced to study Giant Squid Neurons and hope that his experiments indicated something about human neurons as well. The nice thing about giant squid neurons, is that they were large enough to insert the electrical contacts of his day, and so he was able to at least get a sense of how neurons interrelate to each other.
After he had proposed the mathematical model of the Synapse he needed to define a formula for the neuron itself. He suggested a simple aggregate of synapses adjusted for the basic neural logrithmic function.
Marvin Minsky in his book Perceptron however, noted that this model was not accurate because there was a second order response factor not taken into account in the basic Hebb formula. Other Models were suggested, but each could be challenged relatively easily, if only because the models were too simplistic. To this day we still do not have a definitive neuron model, that captures mammalian neurons, let alone human neurons, but we are getting closer to understanding them especially now that we are beginning to learn about the cellular neuroscience at the micro-biological level.
Of recent interest, is the research being done on the connection between the synapse and the DNA which is involved in the growth of synapses and fibrils. While the biochemistry is still somewhat vague, the use of genetic KO techniques has opened up new vistas for understanding the complexity of long-term memory at the cellular level.
The following Models are a transitory look at our current understanding of neural function, undoubtedly future discoveries will change these models and skew them in directions we cannot predict at this time.
* The Biochemistry Model
* The Biological Model
* The Dendrite Model
* The Axon Model
* The Activation Potential Model
* Depolarization vs Partial Depolarization
Neural Network Models of the Neuron
* The Hebb Neuron
* The Hopfield Neuron
* The Weightless Neuron
* The HH Neuron
* The Multi-Layer Network Model
* A Tentative Neural Model
# Artificial Consciousness/Neural Correlates/Neural Models/Axon Model
## The Axon Model

The Concept of a Neuron as being a processing system with inputs a process and outputs, as suggested in the previous article the Dendrite Model gives us an output role. In the neuron this role is taken by the Axon processes of the Neuron.
Theoretically every output is equivalent, however due to technological limitations different technologies have different limits on how many outputs can come from a single source. We call this Fan-out. As a result, only so many outputs can be connected to any signal directly. The Neuron has a 1 to 1 ratio between pre-synaptic buds, and post-synaptic buds on other neurons. In order to achieve this capability the neuron must distribute its signals to the other neurons. This distribution network would theoretically be equivalent to a star shaped network connecting the output of the neuron to the receiving neurons. This structure is called an Outstar.
The limits in the fan-out of the Axon, are related to the ability of the Axon to branch, Only so many branches can be made at any one hub. As a result different output connections have different distances to travel from the hub. As a result over the network of outputs, a signal may arrive at sensitive patches of different neurons at different times. Since this is expected, it is interesting to note that some signals such as the signals called Gamma Synchronized Oscillations manage somehow to synchronize despite such fan-outs.
# Artificial Consciousness/Neural Correlates/Neural Groups/Marr's Codon Model
## Marr's Codon Model

In the 1970s David Marr was studying at Trinity College in Cambridge, and he developed a method of using Probabilistic Mathematical Methods to attempt to understand how some areas of the brain worked. At the time, the reigning theory was that neural connections were mathematical functions, so in his Theory of Cerebral Cortex, for instance, he assumed that the basket cells in Layer 4 of the Cerebral Cortex were Division Functions, and that the connections would work out mathematically to have some meaning beyond his basic Probabilistic Methods. He was sorely disappointed, and died in 1980 before he had published more than a few models of brain functions.
One of the functions he had published was the Theory of Cerebral Cortex article that claimed to have defined the function of the cerebral cortex. Of course since then we have found that his work was not sufficient to capture the whole function of the cerebral cortex. But it was a solid starting point in that it attempted to explain at least the bottom four layers of the cerebral cortex.
Of course Marr was a man before his time, and made a number of unfortunate assumptions, not the least of which being that he could unravel the snarl of connections in the human brain and make sense of it.
One aspect of his research was that he theorized a CODON, a self-classifying Content addressable Memory element as the basis for the Cerebral Cortex. Of course that is not the only function that the cerebral cortex has, so his theory was proven to be inaccurate, but, I feel it is an honest characterization of the Heterogeneous Networks within the first four layers of the cortex, as long as they are isolated from the rest of the brain. Unfortunately for Marr, the Cerebral Cortex is not isolated like his mathematical formulae were, so other functions overlay the basic memory function and complicate proof of its suitability.
One of the main exceptions I have heard of to Marr's work, was experiments done using neural networks that failed to capture certain neural frequencies associated with the real cortex.
Today Marr's article is used as an example of a level of sophistication to reach for, and to cow overenthusiastic graduate researchers. It's main claim to fame however is the fact that it is so dry as to put readers to sleep. My own experience was that the one article put me to sleep 18 times before I read it through once. Some scientists believe that their research is not properly valued if they make it too easy to understand, and so they use literary tricks to increase rather than reduce the complexity of the writing. One way that Marr managed to increase the difficulty of his work was to mystify the reader as to his mathematical functions. cateogry: Neural Networks
# Artificial Consciousness/Neural Correlates/Neural Models/Activation Potential Model
## The Action Potential Model

In the previous couple of sections we have discussed the input and output of the Data processing based model of the Neuron. In this section we need to consider the processing aspect of this model.
The Processing Element of the Data Processing model, sees each neuron as being a type of processor that conditions the data that it is presented, and outputs the conditioned result. How it processes the data, is the question that this model requires us to answer.
The action potential model, therefore describes how the voltage that is accumulated in the cell, is the result of the processing. In this model therefore it is the way that the voltage is changed that defines how the data is processed.
In the Data Processing Model, some of the processing is done in the dendritic mass, and some is done in the actual Soma of the cell where the signals are combined to form a single action potential. Once the signals have been integrated, then they are distributed by the Axon to other neurons. But what is the PROCESS that goes on within the cell?
In order to understand what processing is going on, we need to look not at the physical connections, but at the ways that the voltage is determined. The physical connections form something called a Wired OR structure, The inputs combine to form an output so every positive input adds to the output.
However the changes to the voltage of the cell are not always so simple to describe. For instance some ion channels produce negative inputs, and evidence from logic shows that negating a negative input, often results in something called DeMorganization in which a Wired OR function becomes a NAND function where a negative logic selection function becomes possible. As a result, the brain often works in a negative logic mode to select among signals.
If we however try to interpret the processing as only logical in nature, we find that our model no longer fits the reality, the function of even a three Neuro-Transmitter neuron is a super-set of logic where the function operates on a continuum between OR and NAND that includes functions that are not recognized by logic, if only because they are more inclusive or less inclusive than the classical logic functions could possibly be.
Some of the functions of ion channels are even more complex, because the ion channels themselves might be more complex. For instance some ion channels pump larger ions that have a stronger charge such as the chloride ion and have the result of blocking output of the cell, while other ion channels affect the inner function of the cell, and change the way it reacts to voltage. For instance the S synapse and the NMDA synapses both pump calcium into the cell which reduces Habituation.
# Artificial Consciousness/Neural Correlates/Neural Groups/Inherited Phylogeny Neural Groups
## Inherited Phylogeny Neural Groups

There are probably over 150 different types of neurons in the brain. many of these can be seen to congregate in clusters of a single type of neuron, whether those clusters are layers such as might be found in the cerebral cortex, or Laminae as might be found in the thalamus or hippocampus. Sometimes, the change in type of cell is radical, such as between the hippocampus and its surroundings, and sometimes the change in type of cell is subtle, such as between layer2 and 3 of the cerebral cortex.
Sometimes such as in the Laminae of the thalamus the main difference lies not in the type of cells so much as the fact that they are encapsulated in connective tissue, so that they make up separate clusters of the same type of cell.
I call these Inherited Phylogeny Neural Groups, because the groups form a relatively uniform cluster that all descended probably from the same stem cell, and thus tend to share the same cell phenotype.
As a result of this harmonious state of being, these clusters of cells probably to the same job, just with different data.
# Bible/King James/Two-source Hypothesis

The Two-source hypothesis (or 2SH) is an explanation for the synoptic problem, the pattern of similarities and differences between the three Synoptic Gospels (Matthew, Mark, and Luke). It posits that the Gospel of Matthew and the Gospel of Luke were based on the Gospel of Mark and a hypothetical sayings collection from the Christian Oral Tradition called Q.
This project will be similar to Bible/King James/According to the documentary hypothesis in that it will show the text of the Synoptic Gospels with different colors text for the verses that are unique to each Synoptic, shard by each synoptic, ect. There will also be a three column chart that will compare the verses from the Gospels.

## The Synoptic Gospels side-by side comparison

1. Jesus' Early Life and Ministry
2. The Sermon on the Mount/The Sermon on the Plain
3. Jesus' Further Ministry in Galilee
4. The Way to the Cross

## The individual Synoptic Gospels

* Mark
* Matthew
* Luke
* The Q Source Reconstructed
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