"Caenorhabditis Elegans" and their influence on the research of neural networks.

Elegans worm "Caenorhabditis Elegans" is also an example of why neural networks are so powerful. The 32 olfactory neurons of that worm are connected to 13- 14000 receptors. The neural network of those 32 neurons is taking information from very large areas. And the surface area that delivers information is also important for the neural network. In the case of the elegant worm, the purpose of the network is only to input data to the neural system of that primitive worm. 

Above this text, you can see the neurons of the "Caenorhabditis Elegans". The reason why that worm is not intelligent is that the axons are networked with two main axons. So those neurons have only two states. The more advanced neurons have loop connections to the body of the cells. Or they are forming the loop of interconnected neurons. 

The neurons could have multiple states if it has multiple loop connections in their body. Or the series of neurons are interconnected to a circle. And there is a possibility that the loop of interconnected neurons begins and ends in the same neuron. 

So the olfactory neurons of the elegant worm are the example of the "dummy neural network". The dummy neural network means that the system just collects data from the sensors and maybe sends that data to the screens. Another version of the neural network is the intelligent network. 

There are two main types of neural networks:

1) Passive neural networks which have subtypes: 

1a) Dummy neural network. 

This neural network just collects information from the sensors. 

1b) Intelligent neural networks. 

This neural network preprocesses information before it outputs it. 

2) Active neural networks

Active neural networks are always intelligent. Those neural networks can react to things that they see. In the case of a fire, the system can activate sprinkler systems and order people to get out of buildings. 

That kind of system can detect also things like knives and aim acoustic weapons at the attacker. Or in the cases of the subways, that system can shut down lights in the case of violence. And the security team can use infrared lights in their operations. 

The intelligent network also collects information from the sensors. But there is the preprocessing stage between the output of information. So if we are thinking about surveillance systems that are using the dummy network that system just inputs the film of the surveillance cameras to screens. But the intelligent neural network can also sort the images that the areas where people are more highlighted than areas, there are no people. 

And if there is a person, who seems to want to hide in bushes that system can mark this kind of thing for authorities and security personnel. This is the difference between an intelligent and a dummy network. In those cases the system is passive. It collects information and maybe preprocesses it. But the neural network would not make active actions like using loudspeakers that are telling that the person has the knife or using the acoustic weapon against that kind of target. 

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Living neurons and WARP-speed are the next-generation computing tools.

 

The number of connections determines how powerful a neuron is. 

The image above: a small worm or nematode called "Caenorhabditis Elegans" has only 32 olfactory neurons. They are an example that the number of neurons is not important. The way how to use those neurons is more important. And the number of axons or synapsis is the thing that determines the neuron's ability to collect information and handle it.  

Those 37 neurons are all connected with 14 000 receptors. As you can see from that image those neurons have multiple synapses that are coming from two main synapses. These kinds of neurons are giving information about the hybridization of the non-biological and biological components in the microchips. 

The neuron-based bio-computers or organic will revolutionize computing and machine learning. In this kind of system, the living neurons interact with microchips. The data can transmit to those neurons and out from them by using electric impulses. And those systems can use to teach the neurons. 

The bio-computer means that there are microprocessors with organic layers. In those microchips is the living neurons that interact with the non-organic microchips. Those systems require nutrients for their neurons. And maybe one of the missions of the cleaners is to feed the living neurons in the microchips. 

Feeding the neurons can happen through the algae tank. The nutrient will make those algae live, and then they deliver nutrients to the neurons. That will minimize the risk of virus infection in that system. The virus can destroy the entire neuron core of the processor. 

The idea is that the similar effect that makes it possible that the neuron can change its position in the nervous system can use to train living neurons on the computer platform. And then researchers can transfer those neurons to the damaged brains of humans. But living neurons make it possible that computers can learn spontaneously. 

The use of living neurons in artificial intelligence causes problems in legislation and ethics. We should re-estimate the terms animal and human rights. The high-power artificial intelligence can need similar rights to humans and animals. 

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The WARP speed supercomputer requires that the particle rides with energy waves like in Alcubierre drive. The energy wave can form by using side-coming energy impulses. 

In the simplest version, the WARP-speed supercomputer makes a qubit that rides with an energy wave. So the qubit travels in the fullerene nanotubes or miniaturized particle accelerators. 

The outcoming energy impulses make it possible that the particle or qubit travels faster than the photon travels outside the system. The problem with this system is that the data that travels in the qubits can be disturbed. 

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How does the WARP-speed computer works? 

The description of WARP-speed information transmission is simple  "when the system sends information and photon at the same time to the same track. That information comes to its goal before the photon".  The creation of that kind of data transportation is theoretically a very easy process. The needed thing is the quantum stick. When photon impacts that stick. It will push it forward. 

And then, the end of that stick releases another photon or energy impulse. If the quantum stick is rigid. And quantum fields of that stick will not give in. That makes it possible that information travels in this system faster than free photons can transport it. 

Another version is that the system removes quantum fields around the information channel. The hollow high-power laser and maser rays that are surrounding the fullerene nanotubes can use to remove the quantum fields inside that information channel. The system can use two-layer nanotubes. 

Between those nanotube layers is the layer where the isolation radiation goes. And then the DNA-based rigid superconducting structure transports data. Or another version is that electrons or photons are moving in the WARP channel where all quantum fields are removed which makes it possible that those data transportation can move faster than light travels outside that WARP channel.  

https://eurasiantimes.com/brain-cell-powered-ai-raises-technical-ethical-political-concerns/?amp

https://scitechdaily.com/thousands-of-different-scents-scientists-solve-a-30-year-old-mystery/

https://scitechdaily.com/where-neurons-begin-in-the-brain-isnt-necessarily-where-they-end/

https://en.wikipedia.org/wiki/Alcubierre_drive

https://en.wikipedia.org/wiki/Axon

https://en.wikipedia.org/wiki/Synapse

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The brains are like a quantum computer. But brains are more intelligent and more effective than any quantum computer in the world.

The human brains are the quantum computer. Or they are giving an idea for quantum computers. The thing that makes the human brain so powerful. Is that they begin the data handling process in multiple places at the same time. The neuron is a model of the qubit.

Because it can send the data to multiple axons at the same time. The neuron can select how many axons it uses in each process. The membrane inside the neuron will act as the switch. 

The role of magnesite is that it controls which point the membrane will touch the core of the neuron. But it can also need for short-term memory. When the energy production in the mitochondria is rising that thing makes the magnetite more magnetic. And that thing can benefit in the data transmitted. When the temperature of magnetite would rise they are sending the electromagnetic impulses to the axons. 

That thing is one of the theories of how memory is working. The data from the incoming axon is stored in the magnetite. And then the brain selects should the brain forget the data or should it store it. The idea of memory is to offer a library, where the brain can select the action models in certain situations. 

But the thing is that the axons are acting like a multi-state qubit. The neurotransmitters and the electric processes are giving the multi-layer capacity to the qubit. So the human brains are like the multi-layer qubit system. But the difference between human brains and the quantum computer is that the quantum computer needs many megawatts for the processes. The human brain is using 20 watts for similar processes. 

The thing is what makes this thing possible is that every neuron is like a miniature brain. The neuron can choose how many axons it uses at every action. And another thing is that when the thinking process or data travels through the pineal gland the cerebellum and other parts of the neural system select if the solution is suitable. If the solution is not suitable the data travels in the human brain and in every round the brains are calling more neurons for handling that problem.

This gives more power to the thinking process. But also, some neurons are in reserve for handling the signals that are coming from the senses. The thing that makes human brains so economical is that there are mitochondria in every neuron. And that minimizes the loss of energy. So when the neuron is resting it doesn't use its all mitochondrial capacity. But when a neuron operates, it would increase its power supply by activating more mitochondria. 

In the human mind is three types of memories. The deepest memory is the memory that controls the behavior of species. That genetic memory makes people afraid of things like spiders and snakes. They can be dangerous and that's why humans and other species must be afraid of them. 

The necessary things for surviving the species are stored in DNA. In that molecular memory transmits data over generations. And that data includes information about scary animals like poisonous snakes and spiders. That data is necessary for surviving species. Theoretically is possible to transmit also other skills over the generations by using synthetic DNA bites. 

If that thing is possible to make. It makes it a possible possibility to transfer things like programming skills from one person to another one. By using the DNA bites that are involving those skills. The other two memory types are independent and individual. 

Only DNA-based memory can affect over generations. The DNA is sending RNA for controlling the cells. And then it activates the neuron to react the way, that is typical for species. 

The other memory is the working memory. There are two parts for short-term use. And the purpose of the short-term memory is to operate as the "parking lot". That is the layer, where the brains are choosing the data that is stored in long-term memory. 

And the long-term memory where the data is stored in the chemical form. The brains store some neurotransmitters for use in the future. When some problems are solved often enough, that solution is stored in the long-term memory. 

Sometimes researchers ask. How does the nervous system select, what data is stored? And what data is not so important? Which of those data bites will be written to the DNA which makes them genetically heritable? When brains are storing some data in long-term memory they are pulling axons to that memory block. 

If the memory block is not used the brains remove the axon from that block. And that thing is releasing the reserves of the brains. In the brain are 200 billion neurons and even more axon connections. That number seems impressive. But the capacity of the brain is limited. The process where the brains are removing axons from the memory block is called "forgetting". 

Brains can store the data in two forms. They can store precise answers. Or they can store only the model of how brains created the solution. The last thing is called fuzzy logic. 

That thing makes it possible to use the same models for creating new solutions for the problems. And that means the brains can use fuzzy logic. Fuzzy logic is saving the capacity of the brain. Even if the brains have 200 billion neurons that the number of neurons is limited.

And if the brains would store only precise answers that thing makes them inflexible. If the system uses fuzzy logic. It would turn more flexible and faster because it must not test all neurons whenever it searches for the answer for problems. 

https://scitechdaily.com/scientists-may-have-unlocked-function-of-mysterious-structure-found-on-neurons-in-the-brain/

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