Artificial neuron

Okay kiddo, imagine you have a bunch of toy blocks in different colors and shapes. You also have a toy car that you can make go fast or slow. Now, let's say you want the car to go fast only if there are more blue blocks than red blocks, and slow if there are more red blocks than blue blocks.

An artificial neuron is kind of like this toy car decision maker. It takes in information from lots of different places, like the color and shape of the blocks, and decides how fast or slow the car should go based on that information.

But instead of blocks and a toy car, an artificial neuron works with numbers that are inputted to it. These numbers are called "inputs". Then the artificial neuron uses them to make a decision, which is called an "output".

The neuron does this by "weighing" the inputs. It gives each input a "weight" or importance, and then adds those weights up to make a decision. For example, it might give a weight of 2 to the input for blue blocks, and a weight of -1 to the input for red blocks. Then it would add up all the weighted inputs to decide how fast the car should go.

That's the basic idea of an artificial neuron. By using inputs and weights, it can make decisions based on the information it's given. And when you put a bunch of these neurons together in a group, you can make something called an artificial neural network. It works kind of like a big decision-making machine that can learn and improve over time. Cool, huh?