Inputs $x_i$ are multiplied by weights $w_i$, summed with a bias $b$, and passed through an activation:

• · Inputs $x_i$ — the features.
• · Weights $w_i$ — what the neuron learns.
• · Bias $b$ — shifts the boundary.
• · Activation $\sigma$ — non-linear squash.