Neurons have weight and bias. Weight is multiplied, bias is added. Really that simple.

Here are examples of equivalent notations that may make it more familiar:

  y = 2⋅x + b or
  y = a⋅x + b

- x would be the input to the neuron, often named 'x'.

- a/2 would be the weight, often named 'w'.

- b would be the bias, often named 'b'.

- y is the output.

In neural network documentation this is often written as (often case sensitive, which may be confusing): output = w⋅x + b (output = weight * input + bias).

I hope that explains it.

Bias is just a scalar term that is added. You can learn it via backpropagation like all the other weights.