"Caenorhabditis Elegans" the worm that can't resist its food.

One of the very interesting things about the "Caenorhabditis Elegans" worm is that this kind of creature is one of the examples of the creatures. That has only reflexes. There are only two main axons in those neurons. The input and output axons are making those cells similar to reflex neurons of the human nervous system. 

And that means when "Caenorhabditis Elegans" smells food it cannot resist that smell. Those cells transform that worm into a living missile that is going in the direction of that smell. 

The Elgans worm is an example of an impressive, simple, and effective way to use neurons. The brains of the system are the 32 neurons that are operating multiple sensory cells. The system works very simply. When the worm detects that the smell of food comes from the left side the system activates muscles that the worm starts to turn to that source of the smell.

Image 2) "The Caenorhabditis Elegans"

When the neurons that operate the sensory cells in the middle of the worm's nose activate that worm starts to travel to that food. When the worm travels to food it makes the  worm make "Deep S" shape movements.  

That movement makes it possible for this worm can estimate its distance to food by using triangular metering. The brains of that tiny worm are estimating the angles of different sensory cells. And when the worm is close enough. That makes it to hit to target. 

If human senses are connected directly to the muscles using reflex neurons. Humans would act like this worm. If those reflex neurons connect the sense of hearing to the muscles, the person will do whatever other people say.

If our olfactory coil would be connected to muscles only through the neurons that have in and out connections humans would act like in the cartoon, where the character cannot resist the smell of food. The  "Caenorhabditis Elegans"  is the real-life version of the horror-movie zombie that smells food and then travels to it like some missile. 

"Caenorhabditis Elegans" is one example of reflex automation. When sensors are seeing something the automatic action will make something. One of the examples is the case where water rises too high in some room. That thing causes an action that the system opens ventilation. 

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

Image: 

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

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"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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