The brains will be predicting their perception.

   

 The brains will be predicting their perception.

Brains are predicting their perception. And that thing makes them energy efficient. When the information comes from the senses, it will not activate all neurons immediately. 

 When the signal comes as an example from the eyes to the optic lobe.  At the occipital, there is also a system or a group of neurons which mission is to recognize threats. If those neurons would not recognize the threat they will send that signal to the optic lobe. There the signal will transmit to the activator neurons that are selecting the tracks. 

Where the optic lobe preprocesses and resends senses signals, they reach the activator neurons. Those neurons will not activate all neurons right away. Their mission is to tell can the neuron group handle the data that is sent. 

When the signal comes to neural structure, only a couple of neurons that mission is to route the signal to the right neuron group will activate. They are waiting for the response. And if some neuron group recognizes the signal they send the positive mark. Then the sending lobe would send the standby signal to leading neurons of the neuron group. 

 And then it starts to activate other neurons. In the brain, all neurons do not handle the same things. So the system works like this. The optic lobe sends the part of the sense to the different groups of neurons. And if some group recognizes the sense they are sending the signal "send the rest" and then the optic lobe will send the rest of the data. 

The reason why this thing is made in the brain is simple. The purpose of that thing is to deny that the brains would use too many neurons for some action. And that guarantees that there are always free neurons that can use for some emergency condition. 

The reason why human brains are using the network-type protocol is simple. That means there are many different types of cells with many different types of abilities is to make sure that while the person is thinking the brain can also observe the things that happen around the person. 

The signal must find the right neuron group, and then it will start to activate the other neurons that are handling that kind of data. When the information reaches the right neuron group it will activate the key or leading neuron which, calls other neurons to handle the things. In the computer world, this ability is called "preprocessing". 

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What if the quantum computer can predict how many layers does it need for some data rows?

If quantum computers could predict how many layers or states it needs would make it more effective. That thing makes it possible that the quantum computer can handle multiple problems at the same time. 

If the quantum computers would have a similar process. That allows it to predict how many layers it needs for some processes that thing makes them more effective. In the network-based systems, there might be the pre-selectors that are preprocessing data. In those cases, there could be an AI-based system. 

That selects the necessary computing systems for the processes. The AI-based system would guarantee that the top processors are not overloaded. That system has the descriptions of the problems that are needed the quantum systems.

And if the problem doesn't fill those descriptions, that problem can send to conventional computers. Or the AI can determine how many states quantum computer uses for the problem. But if the calculation will take too long, that second route will send data to the quantum system. Or it can take more layers for handling the more complicated data than predicted. 

Human brains are an impressive tool. They are using less power than some pocket lamps. So modeling the processes in the brain is making it possible to create more effective and compact quantum computers. That can operate on a longer temperature scale. 

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Brains can handle many things at the same time. And they can operate in all temperatures. But the most impressive thing is that human brains are so compact. 

Even the smallest quantum computers are extremely large in comparison with human brains.  So what if we would try to make the computer which is so compact and has the same abilities we are facing one of the biggest problems in the world.

There is the possibility to make artificial neurons by using the nanomachines that are acting like neurons. But the problem is that there needed 200 billion nanomachines for making that thing. 

When we are trying to model the functions of the human brain we must start from the inside out. While that process, we must know that all elements and structures in the brain and body have a purpose. There is an organ called the "pineal gland" inside the human brain. The signal that activates brains is maybe coming from that organ. 

Then the magnetite bites that are in the connection point will anneal by using electromagnetic radiation. When the temperature of the magnetite rises it will strengthen the magnetic field of the magnetite. 

Then that thing pulls the iron bites to the right point of the axon. In some artificial neuron models, that small sodium or iron bites are replaced by using springs.  The neurotransmitters are acting like chemical qubits. There is an element like iron to aim that chemical qubit over the synaptic cleft. And then that neurotransmitter touches the layers of the axon. 

That thing activates the electric reaction. So this reaction can happen when the iron in the neurotransmitter faces the sodium. There might be a series of iron and some neutral atoms. 

And that thing makes it possible to send the electromagnetic impulses in a certain order to axons. So if we can model that kind of process in the quantum computer. We can make the new type of more powerful computers than ever before. This thing is only the model that must handle if somebody is trying to make artificial brains. 

https://www.quantamagazine.org/to-be-energy-efficient-brains-predict-their-perceptions-20211115/

https://thoughtsaboutsuperpositions.blogspot.com/