Analog neural network

Okay kiddo, imagine you have a bunch of little bugs that can only communicate with each other by giving off a certain smell. This smell can either be strong or weak. Now, imagine we want to use these bugs to solve a problem, like figuring out what color a flower is.

An analog neural network is like having a lot of these bugs working together to solve the problem. Each bug represents a neuron, which is like a tiny brain cell that helps us think and process information. These neurons can signal to each other by sending electrical signals, kind of like how the bugs communicate with their smells.

But here's the trick - in an analog neural network, the strength of the electrical signal is important. It can be strong or weak, just like the smell of the bugs. This is important because it helps the network to learn and adapt. If one neuron is getting really strong signals from other neurons, it might get excited and send out strong signals of its own. If another neuron is only getting weak signals, it might not be very excited and might not send out many signals.

All of these signals work together to help the network figure out the problem. In the case of the flowers, each neuron might be responsible for detecting a different color, like red, blue, or green. As the network receives signals from each neuron, it can start to figure out which color the flower is based on all the information it has received.

So, an analog neural network is like having lots of tiny bugs (or neurons) working together to solve a problem. They communicate by sending electrical signals of different strengths, and together they can learn and adapt to figure things out.