The first component of the neuron is a linear function whose output is the sum of the inputs, each multiplied by a coefficient. This function is really more or less a linear regression. These coefficients are typically referred to as weights in neural network speak. For example, given some neuron with the input features of x1, x2, and x3, and output z, this linear component or the neuron linear function would simply be:

Where   are weights or coefficients that we will need to learn given the data and b is a bias term.