Looking at the basic model for a neural network, it is natural to consider: What is the simplest possible neural network? And is such a network useful for anything?

If we have a single layer ($L=1$), only one node in the output layer ($n^1=1$) and no activation function ($g^1$ is the identity function), then we have a very simple neural network. Obviously, having only one input node ($n^0=1$) will be the simplest, but we will initially consider any number of input nodes $n^0 \geq 1$.

With this structure and input to the network given by $x_1, x_2, \ldots, x_{n^0},$ we can compute the output $z$ by

$z = \sum_{j=1}^{n^0} w_j x_j + b.$

(Here we have included a bias $b$, as this was also included in the general model.)

So the output of the network is a *linear* combination of the input values (and the constant $1$).
Furthermore, as seen from the post on the
optimization problem,
the error function is a least squares error function.

This means that the simple network described above is *equivalent* to
linear regression (with a least squares error function, as is the most common).

This means that

- a single input node $n^0=1$ and no bias corresponds to no-intercept simple linear regression,
- a single input node $n^0=1$ and a bias corresponds to simple linear regression,
- any number of input nodes $n^0 \geq 1$ (with or without bias) corresponds to (general) linear regression.