Neurotubule

What are neurotubes? Neurotubule is the name I use to refer to the technology of connecting neural networks to each other, through the creation of “conductors” or “transporters” of data. For me, this is one of the most fascinating aspects of neural network science. I'll explain how it works and how I started using this method myself.

Description of the technology The science of neural networks is developing very quickly. We are currently witnessing researchers attempting to connect networks to each other on a large scale. This is especially important when developing decision-making algorithms. When we use a neural network to solve a problem, we usually represent all the data in one package. However, this is not always sufficient for our purposes. This one big network may no longer be effective and we need to move to another level. To do this, we are creating a second network. We then connect them to each other directly via "neurotubes". This allows us to transfer information from one network to another. This way we can train one network under the control of a large and efficient another. This approach is called "shared link connection". We use single links to connect the two networks to each other. This concept has proven useful in some applications, such as image processing. The essence of the approach is to develop an understanding of the functions of each network and then connect them together to create a multi-network component. So, to summarize, the job of neurotubules is to integrate several individual networks into a common network.

Why are neurotubules needed? When we connect two networks together, we have the opportunity for more complex models. For example, you could add or modify the complexity of a network as you begin to connect it to other networks. The idea here is that a neural network becomes more powerful as it learns more data and experiments with configurations. This network then becomes a powerful decision-making center and provides you with high-quality solutions based on the best and latest research.