The large-size networks can handle information more effectively than small-size networks. The strength of network-based solutions is that malfunction in one data-handling system's part will not cause the end of the operations. In network-based solutions, the loss of one unit will be easier to replace, and damages are not as big as in one centralized solution.
Or actually, the neural network can be virtually centralized. In this kind of system, the user uses the neural network like a centralized system. The user will not see the difference if the centralized or neural-base systems driving that program.
The neural network can be large in two ways. A large number of neurons or actors can make it large. The number of sensors that neural network uses can determine how effectively the system gets information. If the system uses a large-size sensor network it requires lots of calculating or processing capacity.
The image portrays the deep-neural network. In that linear model, the system drives data through the multiple layers where the data-handling units interconnect data for processing information.
That kind of system can recycle data multiple times through it. This makes the system powerful and accurate. And the number of those data handling units makes determines how powerful this system can be.
The geological area where the neural network gets its information determines the effectiveness of the information that the system gets. As an example the system can use thousands of cameras but if they are all pointed at the same point. That thing makes the system ineffective.
If the geological area where the neural network gets information is large, it can get information from various places. In cases where all surveillance cameras in the city are connected to the network that allows the machine can collect information from large-scale areas. Or if many processors in the neural networks make them also more effective.
But when we are thinking about the numeral of neurons in the human brain the ability to use multiple neurons makes the data-handling process less stressful for single neurons. A large number of neurons share the mission with multiple neurons, and that makes missions lighter. Also if one data processing line will blocked other neurons can remove the block. The large number of neurons or data-handling units allows using of multiple routes. And the error management is better in large networks. In large networks, the system can use more connections. And it must not drive all data handling units all the time with full power.
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