Imagine someone at a party deciding whether to jump in the pool. They look around at their friends, and each friend is either urging them on or talking them out of it. They don’t treat every friend equally: a close friend’s opinion counts heavily and a stranger’s barely counts at all. They also bring their own baseline tendency, since some people are more game than others before anyone says a word.
This is a neuron. Each friend’s opinion is an input, how much the person trusts that friend is the weight, and their baseline tendency is the bias. The neuron multiplies each input by its weight, adds them together, and adds the bias. The result is the weighted sum, which you can think of as the total social pressure the person feels.
Pressure is not yet a decision, though. The activation function is the rule that turns that total pressure into what the neuron actually does, and the number it produces is called the neuron’s activation. That activation then becomes one of the inputs for neurons in the next layer, the way one person’s choice to jump becomes pressure on everyone watching.
Different activation functions describe different personalities. A sigmoid is a moderate conformist: it responds gradually to pressure but levels off once it’s fully in or fully out, so more cheering changes nothing. A ReLU ignores discouragement entirely, outputting zero, but responds to encouragement in direct proportion with no ceiling.
The reason activation functions exist at all is that without them, every neuron would simply pass its weighted sum along unchanged, and a network of any depth would behave like one big averaging machine. The activation is the moment a neuron commits to a response, and those commitments, layered on top of one another, are what let a network learn patterns more complicated than an average.