The criterion that can show how advanced the brain is: Neural Complexity

We convey the simulation of the neural complexity by the Nobel laureate, US biologist Gerald Edelman.
neural complexity: complexity between neural connections in the brain. It can be said that the more an isolated neural system differs from its subsets and the connections are both independent and integrated, it is more complex.

gerald edelman has created a simulation to reconcile this complexity and measure with consciousness.

Consider, according to the experiment, three different cerebral cortex areas are simulated. Let the first be an old and diseased brain, the second to be a young and immature brain, and the last to be a normal adult brain. The simulated area contains 512 neuronal groups of three cats, and this area responds to or corresponds to a specific location in the visual field.
With this simulation, a model was created by using the gestalt qualities of seeing (i.e. making sense of images to geuplaya).
According to the result of the simulation, the first example (old brain) represents a cortical area where the connections between different groups of neurons are deliberately reduced. In such an area, individual biological groups are active, but neuron firing is minimal and independent due to the loss (decrease) of connections. the number of connections is very low. The eeg results also show that concurrency has disappeared between these neuronal groups. Since the number of items is high, the entropy of the system is high (that is, the system can enter many states), but because the connections (due to old age) are anatomically decreased, the number of differentiated states is low. The elements of the system are not interacting with each other, the elements in the system do not contribute much to each other and mutual knowledge has no meaning. that is, the complexity of the system is low.
The second example is an immature young cortex where each neuronal group is uniformly linked with other neuronal groups. In simulation, all neuron groups begin to oscillate consistently with each other. the connection between groups is random (think children). the system is highly integrated, but customization has been lost. they all act as a single whole. The number of different situations that the system can take is limited. therefore, its entropy is low. The mutual information between one item in the system and the others is higher than the first example. however, the number of differentiating situations is still not high enough. therefore, the complexity of the system is still low (higher than the first example).
the last example is the normal adult cortex. where the neuron groups are linked. Neurons with similar visual orientations tend to be more interconnected. in this example, the neuron groups are grouped according to both completely consistent (like the second example) and functional properties (unlike the first example). Visual groups with similar orientations are fired more often "concurrently" than those that are functionally unrelated to each other according to the eeg result. entropy of the system is high, if not as much as in the first example. The interaction and knowledge of one element in the system with others is high. hence the overall complexity of the system is high. that is, the differentiation of the system is high. the system can be both integrated (simultaneous) and enter into differentiated different states (dynamically; differentiation is constantly changing).
this explains the above sentence consciously
explains how conscious states can take on billions of situations. each is a conscious state; many neurons are fired simultaneously in different combinations, and related similar groups activate with each other (neural maps).

this simulation also explains the contribution of brain anatomy (such as whether the brain is old or young) to the level of complexity. likewise, the physiological effect of neurons due to the interaction between different groups.

explains the formation of consciousness with high level awareness of neural complexity value. connections are very, meaningful and different; the simultaneous firing and dispersal of different or similar groups and then simultaneous firing in different combinations both explain the state of wakefulness and the conscious states of consciousness of a normal adult individual.

source: gerald edelman's book of consciousness.
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