A neuron is just weighted addition with a decision at the end
An artificial neuron takes a set of numeric inputs, multiplies each one by a weight, adds them all together along with a bias term, and passes the result through an activation function to produce an output. That's the whole mechanism. The weights represent how much each input matters, learned during training, and the bias shifts the whole calculation up or down, letting the neuron activate more or less easily regardless of the inputs.
Think of a neuron deciding whether to recommend a movie based on a few signals: genre match, average rating, and how many friends liked it. Each signal gets multiplied by a weight reflecting how much that neuron 'cares' about it, if genre match matters most, it gets the highest weight. The weighted sum gets combined into a single score, and that score determines the neuron's output.