The neural network is the mind of the tiger. The neural network is the mind of the tiger.

 The neural network is the mind of the tiger. 

The difference between traditional computers and neural networks is the role of the CPU. In traditional computers, the CPU (Central Processing Unit) makes all work, and that causes limits in computing. In neural networks, the CPU preprocesses data that senses send into it. That means the CPU will just recognize the situation and then resend it to a sub-processing unit. The receiving sub-system must have responsibility for that kind of case. That means the subsystem has databases where there are reactions for certain types of events. 

In neural networks, all sub-networks use sorted data. And they are meant to respond to things that senses are sending to the neural network. Each of the subnetworks has certain operational or response areas. And the CPU is like some kind of router. The system finds certain details from the data. Then it can route the information to the right subnetwork. In real life, neural networks could be multiple subnetworks. They can act as AI-based event handlers. 

The complete neural network requires that all sub-networks have also a physical system that runs them. In that case, the system has multiple workstations that run the AI-based systems. In that case, the system will decrease the use of the top CPU. If the system uses multiple independently operating workstations. At can handle multiple problems at the same time. Error detection happens when the neural network uses two or more independently operating data handling units at the same time. 

In an ideal situation, the CPU of the neural network is working as a router that can send information to independently operating subsystems. The independent subsystem has a physical computer and its CPU. In that case, the common CPU of those multiple networked workstations sends a message to a certain subunit, and then the AI-based subunit will make the mission and the system sends the data back to the CPU. 

The shape of the neural network is free. The neural network can be a drone swarm. Or it can be the lots of workstations in some warehouse. The neural networks can collect information from multiple sources. And the neural system's power is that it shares data between multiple computers. The neural network doesn't necessarily learn anything. But it can collect data from very large areas. 

The learning neural network follows some parameters for making its duty. The thing that can be the parameter is the drone that operated the longest time in a certain area. The system can store information about the routes and other things that drones use. Then it can multiply the longest-standing drone's operational profile to another drone. 

And of course system must have multiple parameters like when drones were seen, and where the first impact came. The system interconnects the data from all members of the drone swarm. And then it tries to create an ideal flight path to the target. Here we must remember that the same drone that used to deliver pizza can deliver at least hand grenades. The neural network can interconnect multiple systems like surveillance cameras, satellites, and other things like drone swarms.