Node data structure
(Lemme prime you a little bit: this is about the brain.)
1. Conceptual data
Example:
[Number space]
4
[Object space]
leg
animal
[Attribute space]
cute
soft
[Action space]
move
eat
I think by now, most people can start to imagine a specific object using the ideas above. In my case, it's a cat. But how does it work? The theory is that when an idea fires it causes connected ideas to fire. The level of activation is somewhat lingering and cumulative.
Process of identifying objects:
4-legged fires, ( node 4 and leg can be connected to a single node that is 4-legged )
animal fires,
cute fires,
resulting in high level activation in the node cat. Cat and table connect, eventually, to the the speech muscle memories of cat and table respectively. Theoretically, if these muscle memories also connects to a speech activation unit individually, when both cat and the speech activation unit activate, the word 'cat' is uttered.
2. Visual data
Visual data can probably be connected to conceptual data in the node fashion. But how visual data is construed using node elements needs to be worked out.
Simplified raw image data? ---> different shapes of trees
Geometry data? ---> 3D: trunk + branch + leaves
2D: trunk + top
3. Acoustic data
Needs to be figured out.
4. Lingual data
The word 'fire' is connected to a lot of meanings. But when someone reads the sentence "When an idea fires it causes connected ideas to fire", the idea activate is eventually fired following a network of connection. If someone, who doesn't know about brain cells, walks by a wall with such a sentence written on it, it can be very confusing. Activation lingers, but can be reset when something else draws attention.
Theoretically all the meanings of the word node 'fire' can be divided by whether it's a verb or noun, and be connected to verb or noun respectively, by this the signal can be directed to the correct direction.
5. Negation
Negation mechanism? Negation is very interesting. Theoretically, negation can be done by suppressing the node by means of chemistry. If connected ideas are left activated, something else may come out.
From meaningless homogeneity to meaningful structures
1 cell/cell group = 1 idea. The relative location in the network defines the meaning of a node. The idea cat must be strongly connected to animal, the visual representation of cat, the audio representation of cat sounds and the muscle memory of saying the word 'cat'. Probably also loosely connected to soft, move, cute, etc, as well as some inactive connections, which can become new nodes if necessary. Animal itself is probably also connected to move.
Brain cells in small children have a lot more connection, but it's meaningless to be all connecting. Trimming and connecting means learning. From the structure arises meanings. The structure is where meanings come from.
As long as it works
However the connection is made, whatever the chemistry, as long as it works.
Just a theory
Above is just a theory, needs to be tested.
(Lemme prime you a little bit: this is about the brain.)
1. Conceptual data
Example:
[Number space]
4
[Object space]
leg
animal
[Attribute space]
cute
soft
[Action space]
move
eat
I think by now, most people can start to imagine a specific object using the ideas above. In my case, it's a cat. But how does it work? The theory is that when an idea fires it causes connected ideas to fire. The level of activation is somewhat lingering and cumulative.
Process of identifying objects:
4-legged fires, ( node 4 and leg can be connected to a single node that is 4-legged )
animal fires,
cute fires,
resulting in high level activation in the node cat. Cat and table connect, eventually, to the the speech muscle memories of cat and table respectively. Theoretically, if these muscle memories also connects to a speech activation unit individually, when both cat and the speech activation unit activate, the word 'cat' is uttered.
2. Visual data
Visual data can probably be connected to conceptual data in the node fashion. But how visual data is construed using node elements needs to be worked out.
Simplified raw image data? ---> different shapes of trees
Geometry data? ---> 3D: trunk + branch + leaves
2D: trunk + top
3. Acoustic data
Needs to be figured out.
4. Lingual data
The word 'fire' is connected to a lot of meanings. But when someone reads the sentence "When an idea fires it causes connected ideas to fire", the idea activate is eventually fired following a network of connection. If someone, who doesn't know about brain cells, walks by a wall with such a sentence written on it, it can be very confusing. Activation lingers, but can be reset when something else draws attention.
Theoretically all the meanings of the word node 'fire' can be divided by whether it's a verb or noun, and be connected to verb or noun respectively, by this the signal can be directed to the correct direction.
5. Negation
Negation mechanism? Negation is very interesting. Theoretically, negation can be done by suppressing the node by means of chemistry. If connected ideas are left activated, something else may come out.
From meaningless homogeneity to meaningful structures
1 cell/cell group = 1 idea. The relative location in the network defines the meaning of a node. The idea cat must be strongly connected to animal, the visual representation of cat, the audio representation of cat sounds and the muscle memory of saying the word 'cat'. Probably also loosely connected to soft, move, cute, etc, as well as some inactive connections, which can become new nodes if necessary. Animal itself is probably also connected to move.
Brain cells in small children have a lot more connection, but it's meaningless to be all connecting. Trimming and connecting means learning. From the structure arises meanings. The structure is where meanings come from.
As long as it works
However the connection is made, whatever the chemistry, as long as it works.
Just a theory
Above is just a theory, needs to be tested.
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