In the Brain

When one neuron talks to another, the strength of the signal depends on:

How important or intense that signal is.

Example: If you just touch warm water, the signal is weak. If you touch boiling water, the signal is much stronger.

So, stronger signals = more likely to make the next neuron “fire” (send the message onward).

In Artificial Neural Networks (ANNs)

We copy this idea with weights.

Each input has a weight, which says how important that input is.

Big weight = strong signal → has more influence on the decision.

Small weight = weak signal → less influence.

 Example:
If an ANN is trying to recognize a cat  in a picture:

The pixel showing whiskers might get a high weight (important clue).

The pixel showing just the background wall gets a low weight (not important).

So, “some signals are stronger than others” means that both in the brain and in ANNs, not every piece of information matters equally. Some clues are more important and carry more “power” in decision-making.

Cat features (like whiskers, ears, eyes, fur) → very important → strong signals.

Background wall (plain surface behind the cat) → not important for knowing it’s a cat → weak signal.