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